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Energy capture and storage

Energy capture & storage

Develop cleaner, safer energy materials with digital chemistry

Request a demo
Develop cleaner, safer energy materials with digital chemistry

Discover and optimize energy materials at the molecular level

Safer, cheaper, and more effective batteries, fuel cells, and supercapacitors are critical in overcoming societal ecological challenges in the automotive, aviation, and energy industries.

Schrödinger’s Materials Science platform provides the tools to model materials at the molecular level, using computational power to drive forward the development of cleaner, lighter, safer, more energy-efficient, and lower cost materials for batteries, fuel cells, and photovoltaics – ready to power the next generation of innovation.

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Intuitive computational workflows designed by energy materials experts

Easy-to-use system builders for all material types
Powerful workflows for physics-based simulation, machine learning, and data analysis
Dedicated customer support and extensive training resources

Your toolkit for energy materials innovation

Predict key properties for batteries, fuel cells, photovoltaics, and hydrogen storage R&D

  • Explore electrode, electrolyte, and solid electrolyte interphase (SEI) properties such as redox potentials and ion mobility (diffusivity and coordination environments) for battery materials
  • Optimize photovoltaic material properties and performance metrics for semiconductors, photosensitive materials, perovskites, and organic photovoltaics
  • Elucidate chemical reaction profiles for energy storage processes, catalytic mechanisms, and degradation pathways
  • Predict hydrogen (or other small molecule) molecular mobility and stability in storage materials

Accelerate new materials discovery with high-throughput screening and machine learning

  • Run high-throughput screening of new materials candidates to identify the best performers
  • Assess new catalysts for energy-related transformations, such as electrolytic hydrogen production
  • Screen electrolyte properties relevant to SEI formation

Enable access to digital materials design through a centralized informatics platform

  • Bridge the gap between experimental and computational data
  • Drive faster and better materials design with real-time access to predictive models
  • Enhance collaboration and decision-making across your enterprise

Our platform in action

Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Fast, accurate, and tunable: Advancing battery materials innovation with Schrödinger’s Machine Learning Force Fields

Join us for live demos showcasing applications of MLFFs for accurate modeling of complex systems including liquid and solid-state electrolytes.

Materials Science Webinar

Fast, accurate, and tunable: Advancing battery materials innovation with Schrödinger’s Machine Learning Force Fields recording

Join us for live demos showcasing applications of MLFFs for accurate modeling of complex systems including liquid and solid-state electrolytes.

Materials Science Webinar

Advancing battery materials innovation using charge-aware machine learning force fields

In this webinar, we will demonstrate how Schrödinger is utilizing an integrated computational approach combining physics-based molecular modeling with machine learning force fields (MLFFs) to address key challenges in battery materials design.

Materials Science Webinar

Accelerating materials discovery with physics-informed AI/ML

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Materials Science Webinar

Advancing machine learning force fields for materials science applications

In this webinar, we will introduce Schrödinger’s state-of-the-art MLFF architecture, called Message Passing Network with Iterative Charge Equilibration (MPNICE), which incorporates explicit electrostatics for accurate charge representations.

Materials Science Webinar

Accelerating chemical innovation with AI/ML: Breakthroughs across materials applications

In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications.

Materials Science Webinar

High-performance materials discovery: A decade of cloud-enabled breakthroughs

This talk will showcase how Schrödinger’s integrated materials science platform enables massive parallel screening and de novo design campaigns across diverse applications.

Materials Science Webinar

Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform

In this webinar, we demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.

Materials Science Webinar

How Physics-based Modeling and Machine Learning Enable Accelerated Development of Battery Materials

In this webinar, we focus on examples to demonstrate the application of automated solutions for accurate prediction of thermodynamic stability and voltage profile of cathode materials, ion diffusion pathways and kinetics in electrode materials, transport properties of liquid electrolytes and modeling the nucleation and growth of solid electrolyte interphase (SEI) layers using Schrödinger’s SEI simulator module.

Materials Science Webinar

AI/ML meets physics-based simulations: A new era in complex materials design

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices. 

Featured courseMolecular modeling for materials science applications: Battery materials course

Molecular modeling for materials science applications: Battery materials course

Online certification course: Level-up your skill set in battery modeling

Learn how to apply industry-leading computational software to  predict key properties of organic and organometallic compounds, determine transition state and generate reaction profiles with automated workflows and machine learning models.

  • Self-paced learning content
  • Hands-on access to Schrödinger software
  • Guided and independent case studies
Learn More

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Catalytic Selectivity Through Microkinetic Modeling

Learn to analyze the selectivity of the catalytic oxidation of CO and H2 on a Pd(111) surface using Microkinetic Modeling (MKM) calculations.

Materials Science Tutorial

Ionic Conductivity

Learn to calculate the ionic conductivity.

Materials Science Tutorial

Locating Adsorption Sites on Surfaces

Learn how to locate adsorption sites on surfaces.

Materials Science Documentation

Machine Learning Force Fields

Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.

Materials Science Tutorial

Machine Learning Force Field

Learn how to use machine learning force field optimization methods to prepare and simulate various systems.

Materials Science Documentation

MS Transport

Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity and diffusions of atoms and molecules.

Materials Science Documentation

MS Surface

A solution for heterogeneous catalysis and materials processing.

Materials Science Documentation

MS Reactivity

Automated workflows for design, optimization, and unsupervised mechanism discovery in molecular chemistry.

Materials Science Documentation

MS Reactive Interface Simulator

Generate physically relevant electrode-electrolyte interface morphologies for batteries.

Materials Science Documentation

MS Informatics

Automated machine learning tools for materials science applications

View more tutorials

Key products

Learn more about the key computational technologies available to progress your research projects.

MS Formulation ML

Automated machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties

Virtual Cluster

Secure, scalable environment for running simulations on the cloud

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

MS Informatics

Automated machine learning tools for materials science applications

Desmond

High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy

OPLS4 & OPLS5 Force Field

A modern, comprehensive force field for accurate molecular simulations

MS Transport

Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity, conductivity and diffusions of atoms and molecules

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

MS Reactive Interface Simulator

Generate physically relevant electrode-electrolyte interface morphologies for batteries

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

Insights into electrolyte reactivity at the Li metal surface from density functional theory

Materials Science Publication

Designer Fluorescent Redoxmer Self-Reports Side Reactions in Nonaqueous Redox Flow Batteries

Materials Science Publication

Computational and Machine Learning-Assisted Discovery and Experimental Validation of Conjugated Sulfonamide Cathodes for Lithium-Ion Batteries

Materials Science Publication

Towards long-life 500 Wh kg−1 lithium metal pouch cells via compact ion-pair aggregate electrolytes

Materials Science Publication

Robust and effective ab initio molecular dynamics simulations on the GPU cloud infrastructure using the Schrödinger Materials Science Suite

Materials Science Publication

Machine learning for data-driven design of high-safety lithium metal anode

Materials Science Publication

Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase

Materials Science Publication

Towards the 4 V-class n-type organic lithium-ion positive electrode materials: the case of conjugated triflimides and cyanamides

Materials Science Publication

Accurate quantum chemical reaction energies for lithium-mediated electrolyte decomposition and evaluation of density functional approximations

Materials Science Publication

Improvement of electrolytes for aluminum ion batteries: A molecular dynamics study

View more publications

Schedule a consultation on Schrödinger’s battery solutions.

Contact us today to explore how you can leverage advanced simulation and AI/ML for battery materials.

Don’t see your areas of interest above? Reach out so we can help.

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Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Experimental Chemistry

Experimental Chemistry

Request a demo
Experimental Chemistry

Enhance materials discovery, analysis, and optimization with scientifically-validated digital solutions

The field of experimental chemistry is rapidly transforming, thanks to digital technologies that can quickly test a hypothesis or rapidly iterate on an idea. These technologies can help speed the identification of the best molecules, formulations, or processes in complex materials systems.

As an experimental chemist, you know that efficiency matters and that’s why an ever-growing list of companies and institutions are adopting molecular simulation as part of their innovation practice to uncover faster and more cost-efficient materials innovations.

Advantages of the Schrödinger platform for experimentalists

Explore more target candidates in less time

  • Quickly screen large chemical spaces and identify promising candidates with target properties and performance
  • Greatly reduce time and cost on experimental trials and focus on the right problems and solutions

Gain deeper insights with fewer unknowns

  • Get a deep understanding of the “what” and the “why” at an atomic level of your materials systems with best-in-class, physics-based models
  • Generate insights into the unknown and confidently improve existing chemistry or pursue novel chemistry with advanced machine learning methods

Go beyond the limits of experiments

  • Test ideas that are challenging or impossible through traditional methods of experimentation
  • Complement your experiments with molecular simulation and machine learning to explore far beyond known chemical space and improve existing materials or generate novel ideas for new materials

Enhance your materials analysis capabilities

Improve your understanding of existing or new materials with simulated spectral data (e.g. NMR, XRD, IR)

Leverage our team of experts

Transform your on-boarding experience of new software with Schrödinger’s team of experts in computational chemistry offering dedicated technical and scientific support, and personalized training

>Read the article

Benefit from large collection of online learning resources

Access vast online education resources, including tutorials and online courses, facilitating rapid upskilling of your team, including experimentalists who are new to computational chemistry

Learn more about our solutions for:

Polymeric Materials
Pharmaceutical Formulations & Delivery
Energy Capture & Storage
Organic Electronics
Consumer Packaged Goods
Thin Film Processing
Catalysis & Reactivity
Metals, Alloys & Ceramics

Technology in action

View more articles
FeaturedMolecular modeling for materials science applications

Online certification course: Level-up your skill set in materials innovation

Not familiar with Schrödinger software and interface? Benefit from vast educational resources, self-paced courses, and 1-1 training tailored for you. Schrödinger software is designed for experts and novices with easy-to-use interface and automated workflows, backed by dedicated scientific and technical support.

Learn More

You’re in good company

“I tried a few different modeling platforms, but as an experimentalist with no background in computational chemistry and limited coding experience, I found Schrödinger’s software the easiest to use.”
Jessica GoldenDirector of R&D, Sepion Technologies
“I’ve found that our customers in the mining sector don’t always understand the value of digital simulation — until they see it for themselves.”
Andrew JacksonScientist, Solvay
“By working closely with Schrödinger experts, we were impressed by how fast we were able to learn to apply molecular simulations, even with no prior modeling experience.”
Martin SettleSenior Research Manager, Polymer Science Sustainability & Packaging, Reckitt

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Materials Engineering

Materials Engineering

Request a Demo
Materials Engineering

Tackle materials engineering challenges with atomic-level simulations

A deep understanding of the fundamental structure, composition, and behavior of materials at the atomic scale is crucial for materials engineering. This understanding guides engineers as they tailor material properties to meet specific performance requirements.

Schrödinger’s Materials Science platform enables engineers to drive innovation and solve complex problems by providing atomic-level insight into their materials and the processing-structure-property relationships that govern them.

Advantages of the Schrödinger platform for materials engineers

Understand the fundamental science behind specific engineering problems

  • Leverage atomic-scale simulation and machine learning to identify potential microscopic causes behind manufacturing challenges
  • Investigate atomic-scale behaviors that are impractical to diagnose experimentally

Optimize materials processing and manufacturing strategies

  • Use large-scale digital screening to efficiently select the most optimal materials for performance and processability
  • Enable reliable decision-making through the predictive modeling of end product properties

Leverage our team of experts

Benefit from Schrödinger’s team of computational chemistry experts offering dedicated technical and scientific support, as well as personalized training

Benefit from our large collection of online learning resources

Access vast online education resources, including tutorials and online courses, facilitating rapid upskilling of your team, including experimentalists who are new to computational chemistry

Learn more about our solutions for:

Polymeric Materials
Pharmaceutical Formulations & Delivery
Energy Capture & Storage
Organic Electronics
Consumer Packaged Goods
Thin Film Processing
Catalysis & Reactivity
Metals, Alloys & Ceramics

Technology in action

View more articles
FeaturedMolecular modeling for materials science applications

Online certification course: Level-up your skill set in materials innovation

Not familiar with Schrödinger software and interface? Benefit from vast educational resources, self-paced courses, and 1-1 training tailored for you. Schrödinger software is designed for experts and novices with easy-to-use interface and automated workflows, backed by dedicated scientific and technical support.

Learn More

You’re in good company

“I tried a few different modeling platforms, but as an experimentalist with no background in computational chemistry and limited coding experience, I found Schrödinger’s software the easiest to use.”
Jessica GoldenDirector of R&D, Sepion Technologies
“I’ve found that our customers in the mining sector don’t always understand the value of digital simulation — until they see it for themselves.”
Andrew JacksonScientist, Solvay
“By working closely with Schrödinger experts, we were impressed by how fast we were able to learn to apply molecular simulations, even with no prior modeling experience.”
Martin SettleSenior Research Manager, Polymer Science Sustainability & Packaging, Reckitt

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Metals, Alloys and Ceramics

Metals, Alloys & Ceramics

Uncover the next generation of inorganic materials with digital chemistry

Request a demo
Digital chemistry for materials design

Discover and optimize inorganic materials at the molecular level

Structures, morphologies, and compositions are important factors for the properties of inorganic materials, and understanding structure-property relationships at the atomic level is critical for optimal materials design.

Leverage Schrödinger’s Materials Science platform to perform efficient multiscale simulations (quantum mechanics, ab-initio, and molecular dynamics) and build machine learning models to accurately predict these key properties, enabling the design of high-performance inorganic materials.

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Intuitive computational workflows designed by experts in inorganic materials

Easy-to-use system builders for all inorganic material types
Powerful workflows for molecular simulation, machine learning, and data analysis
Dedicated customer support and extensive training resources

Your toolkit for inorganic materials innovation

Gain a deeper understanding of inorganic materials properties

  • Explore the mechanical, magnetic, and dielectric properties of organic and inorganic materials, their surfaces, and interfaces
  • Establish relationships between structure, composition, dimensionality, and key materials parameters
  • Elucidate diffusion, segregation, and intercalation reaction mechanisms
  • Reveal the structure of grain boundaries and dislocations
  • Evaluate factors affecting thermodynamic stability
  • Uncover effects of doping and point defects in semiconductors
  • Understand phase diagrams and mechanisms for phase transformations

Enhance your materials analysis capabilities

  • Enable comprehensive analysis using band structures and projected densities of states, phonon analysis, and free energies and equations of states
  • Simulate Infrared, Raman, and solid-state NMR spectra
Featured courseMolecular modeling for materials science applications

Molecular modeling for materials science applications: Surface chemistry

Online certification course: Level-up your skill set in materials surface modeling

Learn how to apply industry-leading computational software to predict key properties of reaction in solids and on surfaces for bulk crystals and inorganic solids with automated workflows and machine learning models.

  • Self-paced learning content
  • Hands-on access to Schrödinger software
  • Guided and independent case studies
Learn More

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Catalytic Selectivity Through Microkinetic Modeling

Learn to analyze the selectivity of the catalytic oxidation of CO and H2 on a Pd(111) surface using Microkinetic Modeling (MKM) calculations.

Materials Science Documentation

MS Surface

A solution for heterogeneous catalysis and materials processing.

Materials Science Documentation

MS Microkinetics

An efficient tool for surface reaction kinetics.

Materials Science Documentation

Quantum ESPRESSO Interface

A comprehensive graphical user interface for calculation set-up, job control and results analysis.

Materials Science Tutorial

Introduction to Materials Science Maestro Tutorial

An introduction to Materials Science Maestro, covering basic navigation, an intro to building models and several of the key functionalities of the graphical user interface.

Materials Science Documentation

Materials Science Documentation

Comprehensive reference documentation covering materials science panels and workflows.

Materials Science Tutorial

Electronic Structure Calculations of Bulk Crystals Using Quantum ESPRESSO

Learn the basics of the Quantum ESPRESSO interface for periodic density functional theory (DFT) calculations of bulk solids, including convergence testing, geometry optimization, band structures, the density of states (DOS), and the projected density of states (PDOS).

Materials Science Tutorial

Atomic Layer Deposition

Tutorial to show how to use adsorption tools to model atomic layer deposition (ALD) processes.

Materials Science Tutorial

Python API for Materials Science (Part 1: Working with Molecules)

Learn some typical modules and perform exercises for an introduction to the Python API for materials science.

Materials Science Documentation

Materials Science Panel Explorer

Quickly learn which Schrödinger tools are the best fit for your research.

View more tutorials

Key Products

Learn more about the key computational technologies available to progress your research projects.

Virtual Cluster

Secure, scalable environment for running simulations on the cloud

Quantum ESPRESSO Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

Isotropic atomic layer etching of GaN using SF6 plasma and Al(CH3)3

Materials Science Publication

Structural investigation, quantum chemical calculation, energy framework analysis and MIC studies of silver and cobalt complexes of 4-amino-N-(4, 6-dimethyl-2 pyrimidinyl) benzenesulfonamide in presence of secondary ligand

Materials Science Publication

Synthesis, Crystal Structure, and Computational Investigations of 2-(2-(4-Fluorophenyl)-2-oxoethyl)-6-methyl-5-(4-methylbenzyl)pyridazin-3(2H)-one as Antiviral Agent

Materials Science Publication

Ionic liquid as an effective green inhibitor for acid corrosion of aluminum composite: experimental and theoretical considerations

Materials Science Publication

Effect of External Pressure and Quantum State on the Local Magnetization of Germanium Layers: Ab Initio Calculation

Materials Science Publication

Benchmarking Machine Learning Descriptors for Crystals

Materials Science Publication

Through-Bond-Driven Through-Space Interactions in a Fullerene C60 Noncovalent Dyad: An Unusual Strong Binding between Spherical and Planar ‘ Electron Clouds and Culmination of Dyadic Fractals

Materials Science Publication

Quenching-Resistant Solid-State Photoluminescence of Graphene Quantum Dots: Reduction of –? Stacking by Surface Functionalization with POSS, PEG, and HDA

Materials Science Publication

On Achieving High Accuracy in Quantum Chemical Calculations of 3d Transition Metal Systems: A Comparison of Auxiliary-Field Quantum Monte Carlo with Coupled Cluster, Density Functional Theory, and Experiment for Diatomic Molecules

Materials Science Publication

Adsorption properties of graphene towards the ephedrine – A frequently used molecule in sport

View more publications

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Consumer Packaged Goods

CONSUMER PACKAGED GOODS

Harness molecular simulation to accelerate innovation for consumer goods

Request a demo
Consumer Packaged Goods

Overview

Molecular modeling and simulation provide new opportunities to accelerate R&D product development, rationalize  behavior at the nanoscale, optimize manufacturing processes, and reduce costs by offering insights into the atomic-level properties that impact product performance.

With Schrödinger’s digital chemistry platform, you can access extensively validated tools to predict key performance indicators for consumer products. By building and simulating complex molecular systems that directly relate to product formulas, you can make informed decisions and create game-changing ingredients and products in response to the rapidly changing trends in consumer goods markets.

Design, develop, & optimize for a full range of consumer products

Consumer Goods

Meet consumer demands with molecular-level insights

Predict physicochemical, morphological, optical, interfacial, and sensory properties for:

  • Ingredient selection
  • Product formulation development, modeling complex formulations with all the critical ingredients 
  • Optimization of processing conditions at multiple scales, from bench to pilot plant to factory
  • Product stability by studying product-packaging interactions
  • Product performance by modeling products in action
Food & Beverage

Enable rational design of healthier, tastier foods

Your ability to design the next generation of sustainable food starts at the molecular level

  • Understand how ingredient interactions and product stability impacts shelf life by predicting degradation, compatibility, and phase behavior
  • Optimize food and beverage processing conditions by looking at phase behavior, pressure, and temperature
  • Study how proteins interact with ingredients and how unfolding affects food texture
  • Understand the molecular basis of odor and flavor molecular activation
  • Study the behavior of micro- and nano-emulsions
Cosmetics & Personal Care

Transform cosmetic and personal care product development with digital chemistry

Accelerate product formulation design by leveraging virtual testing of product performance at the nanoscale

  • Computationally explore formulation design space of cosmetics, fragrance, and personal care products before running experiments
  • Predict chemical and optical stability of ingredients and explore their degradation
  • Understand complex emulsion behavior and stability at multiple scales including morphology
  • Mimic product testing using biological interface models to gain insight to a range of properties (physical to sensory)
  • Derisk bio-based drop-in replacement ingredients using physics-based simulations to ensure compatibility and product performance
Cleaning Products

Design sustainable, efficient cleaning products at the molecular level

Transform cleaning product development with digital chemistry

  • Gain molecular insight into antimicrobial mode action that mimics experiment (i.e. electroporation of microbial membranes with antimicrobial actives)
  • Design next-generation, sustainable active ingredients
  • Predict performance on different surfaces like fabrics and hard surfaces
  • Develop machine learning models for assessing safety (e.g. toxicity)
Packaging Materials

Accelerate the development of sustainable packaging materials

Leverage powerful digital simulations to accelerate discovery of novel packaging materials

  • Understand chemical compatibility between packaging and product ingredients and formulations
  • Virtually test the key properties of new sustainable packaging materials (mechanical, transport properties, moisture sensitivity)
  • Perform life cycle assessment analysis of packaging materials
  • Gain novel insights into product-packaging interactions and how they affect shelf life

Technology in action

Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Transforming Clean Label Innovation in FMCG via Physics-Powered AI and Predictive Modeling

Join us for a webinar with Innovation Research Interchange and learn how the Fast-Moving Consumer Goods (FMCG) sector is currently navigating a significant transition driven by a global consumer shift toward “clean label” products and high-transparency ingredient lists.

Materials Science Webinar

Accelerating Product Development: The Industrial Shift to AI/ML-Driven Formulation

In this discussion, we explore the rapidly evolving role of modeling and machine learning in formulation design; from a supplementary tool to a driving force of innovation.

Materials Science Webinar

Accelerating materials discovery with physics-informed AI/ML

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Materials Science Webinar

Advancing machine learning force fields for materials science applications

In this webinar, we will introduce Schrödinger’s state-of-the-art MLFF architecture, called Message Passing Network with Iterative Charge Equilibration (MPNICE), which incorporates explicit electrostatics for accurate charge representations.

Materials Science Case Study

Advancing sustainable food processing through integrated experimental and molecular simulation approaches

Scientists from Schrödinger and UMass carried out comprehensive studies experimentally and computationally to investigate the key properties and extrusion performance of zein-formulated meat alternatives.

Materials Science Webinar

Accelerating chemical innovation with AI/ML: Breakthroughs across materials applications

In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications.

Materials Science Webinar

AI/ML-Powered Formulation Design: Accelerating Innovation

Schrödinger is excited to be hosting a webinar with C&EN on May 29th.

Materials Science Case Study

The Future of Food: Molecular Simulations and AI/ML Reshaping Product Development

Materials Science Webinar

High-performance materials discovery: A decade of cloud-enabled breakthroughs

This talk will showcase how Schrödinger’s integrated materials science platform enables massive parallel screening and de novo design campaigns across diverse applications.

Materials Science Webinar

Virtual testing of personal care and cosmetics formulations using digital chemistry methods

FEB 19, 2025 | ケーススタディを通じて、計算化学が製品開発、容器設計、製品使用時の解析にどのように役立つかを示します。

Featured courseMolecular modeling for materials science applications

Molecular modeling for materials science applications: Consumer packaged goods course

Online certification course: Level-up your skill set in consumer goods product modeling

Learn how to apply Schrödinger’s industry-leading software to predict key properties of simple and complex material formulations for consumer goods with automated workflows and machine learning models.

  • Self-paced learning content
  • Hands-on access to Schrödinger software
  • Guided and independent case studies
Learn More

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Optimizing Viscosity and Cost in Formulations with Missing Structural Data

Learn to build a machine learning (ML) model to predict cost and viscosity of shampoo formulations with missing structural data.

Materials Science Tutorial

Building and Analyzing a Complex Lipid Bilayer and Embedding a Membrane Protein

Learn to build and analyze a complex lipid bilayer and how to embedd a protein.

Materials Science Documentation

Complex Bilayer Builder Panel

Build single or multi-component lipid membranes with or without an embedded membrane protein.

Materials Science Documentation

Membrane Analysis Panel

Calculate structural properties for a lipid membrane over the selected frames of a trajectory.

Materials Science Documentation

Membrane Analysis Viewer Panel

View plots of the structural properties of a lipid over the course of a molecular dynamics trajectory, generated using the Membrane Analysis panel.

Materials Science Documentation

Machine Learning Force Fields

Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.

Materials Science Tutorial

Machine Learning Force Field

Learn how to use machine learning force field optimization methods to prepare and simulate various systems.

Materials Science Documentation

MS Transport

Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity and diffusions of atoms and molecules.

Materials Science Documentation

MS Penetrant Loading

Molecular dynamics (MD) modeling for predicting water loading and small molecule gas adsorption capacity of a condensed system.

Materials Science Documentation

MS Informatics

Automated machine learning tools for materials science applications

View more tutorials

Key Products

Learn more about the key computational technologies available to progress your research projects.

MS Formulation ML

Automated machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties

Virtual Cluster

Secure, scalable environment for running simulations on the cloud

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

MS Penetrant Loading

Molecular dynamics (MD) modeling for predicting water loading and small molecule gas adsorption capacity of a condensed system

Desmond

High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy

MS CG

Efficient coarse-grained (CG) molecular dynamics (MD) simulations for large systems over long time scales

OPLS4 & OPLS5 Force Field

A modern, comprehensive force field for accurate molecular simulations

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

MS Transport

Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity, conductivity and diffusions of atoms and molecules

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

Screening Antioxidant Ingredients Using Quantum Mechanics and Machine Learning

Materials Science Publication

Uncovering the light absorption mechanism of the blue natural colorant allophycocyanin from Arthrospira platensis using molecular dynamics

Materials Science Publication

Taste-Guided Isolation of Bitter Compounds from the Mushroom Amaropostia stiptica Activates a Subset of Human Bitter Taste Receptors

Materials Science Publication

Gaining molecular insights towards inhibition of foodborne fungi Aspergillus fumigatus by a food colourant violacein via computational approach

Materials Science Publication

Improving color and digestion resistibility of 3D-printed ready-to-eat starch gels using anthocyanins

Materials Science Publication

Molecular insights into the structure forming properties of zein and a rheological comparison with hordein

Materials Science Publication

Steviol rebaudiosides bind to four different sites of the human sweet taste receptor (T1R2/T1R3) complex explaining confusing experiments

Materials Science Publication

Elucidation of the sweetening mechanism of sweet orange fruit aroma compounds on sucrose solution using sensory evaluation, electronic tongue, molecular docking, and molecular dynamics simulation

Materials Science Publication

Complexation mechanisms of aqueous amylose: Molecular dynamics study using 3-pentadecylphenol

Materials Science Publication

Nanoscale analysis of plastic contaminants migration in packaging materials and potential leaching into model food systems

View more publications

Schedule a consultation on Schrödinger CPG solutions

Contact us today to discuss how you can leverage molecular modeling and AI/ML to stay ahead in today’s fast-evolving consumer goods market.

Don’t see your areas of interest in the current lists above? Reach out so we can help.

Form submitted

Thank you, we’ll be in touch soon.

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

MS – Computational Chemistry

Computational Chemistry

Request a demo
Computation chemistry

Adopt high-impact modeling to power your materials R&D

While computational chemistry has long been a part of the material R&D process, we’re in the midst of a paradigm shift from computer-aided to computer-driven materials discovery.

With Schrodinger’s digital platform for molecular simulation, you can take advantage of high-performance physics-based modeling and machine learning technologies that level up your design and discovery pathways and empower you to deliver real material R&D innovations.

Advantages of the Schrödinger platform for computational chemists

Decades of innovation at your fingertips

Benefit from technology backed by 30 years of scientific R&D and validated by thousands of customers across industries, with constant software improvement according to user feedback

Speed, accuracy and performance with GPU acceleration

Ensure you can deliver results and meet project timelines – with accelerated GPU-performance, delivering speed, accuracy and functionality.

Single user interface to access the spectrum of simulation capabilities

Access powerful quantum mechanics (QM), both molecular and periodic, molecular dynamics (MD) simulations, both all-atom and coarse-grained and machine learning (ML) from a single intuitive interface, MS Maestro, with automated workflows.

Easily automated modeling workflows

Leverage the Schrödinger Python API to automate modeling capabilities using the universal scripting language, Python

Supported by a team of experts

Transform your on-boarding experience of new software with Schrödinger’s team of experts in computational chemistry offering dedicated technical and scientific support, and personalized training

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Large collection of resources for online learning

Access vast online education resources, including tutorials and online courses, facilitating rapid upskilling of your team, including experimentalists who are new to computational chemistry

A broad range of molecular modeling & machine learning capabilities

Schrödinger offers a broad range of advanced software solutions to support materials scientists and engineers.

Density functional theory with gaussian orbital-based and plane-wave basis methods 
Molecular mechanics featuring accurate conformation search algorithm
Advanced all-atom force field for molecular dynamics simulations
Materials informatics with automated library generation algorithms
Accelerated quantum chemistry with extended tight-binding methods
Classical molecular dynamics for all-atom and coarse-grained representations 
Machine learning with support for active learning algorithms and deep neural network

Technology in action

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Molecular modeling for materials science applications

Online certification course: Level-up your skill set in materials innovation

Not familiar with Schrödinger software and interface? Benefit from vast educational resources, self-paced courses, and 1-1 training tailored for you. Schrödinger software is designed for experts and novices with easy-to-use interface and automated workflows, backed by dedicated scientific and technical support.

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You’re in good company

“Using Schrödinger’s digital molecular simulation platform, we’ve explored thousands of new materials in silico and used that exploration to select the most likely candidates to improve LMB cell performance and stability. This approach has led to a 10-fold improvement in our battery performance over the past two years.”
Jessica GoldenDirector of R&D, Sepion Technologies
“In contrast to current computational methods that rely on rudimentary open source molecular descriptors and highly variable, limited public databases, this novel approach utilizes Schrödinger’s physics-based modeling to calculate more comprehensive descriptors and harnesses Eonix’s chemically diverse database for robust machine learning model training.”
Don DeRosaCo-founder, Eonix
“On average, applying Schrödinger’s technology has expedited timelines up to 10x compared to a purely experimental approach.”
Martin SettleSenior Research Manager, Polymer Science Sustainability & Packaging, Reckitt
“By combining the speed of machine learning with the accuracy of Schrödinger’s physics-based simulation methods, Cambrium is tapping into a new world of potential for novel biomaterials.
Pierre SalvyHead of Engineering, Cambrium GmbH

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Complex Formulations

Complex Formulations

Deliver industrial formulations with optimized product properties

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Deliver industrial formulations with optimized product properties

Optimize your formulations at the molecular level

Complex and evolving structures, often in fluid states, play a crucial role in many industrial processes across the pharmaceutical, consumer product, plastic, composite, and petrochemical industries.

With Schrödinger, you have validated workflows and expert support to optimize the properties of your end formulation products. Use digital solutions to rigorously select and combine the right ingredients in the right manner. Moreover, Schrödinger’s atomistic and coarse-grained models enable characterization of molecular interactions and nanoscale structuring for much larger molecular systems (millions of atoms or particles), compared with other commercially available software.

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Intuitive computational workflows designed by experts in formulation chemistry

Easy-to-use and industry relevant system builders for complex formulations of large molecular systems
Powerful workflows for molecular simulation, machine learning, and data analysis
Dedicated customer support and extensive training resources

Digital solutions for complex formulations

Predict key properties with automated workflows

  • Chemical stability and interaction, and ingredients miscibility
  • Elastic constants (e.g., bulk modulus, shear modulus, etc.), glass transition temperatures (Tg), diffusion constants, melting points, and solubility parameters
  • Micelle formation, morphology, and self assembly of emulsions
  • Water uptake and transport in formulations
  • Phase diagrams, multi-component separation/aggregation/dissolution behaviors

Accelerate ingredients selection with high-throughput screening and machine learning

  • Screen ingredients by simulating the performance of formulation products
  • Design new chemistries from alternative sources, for example, to ensure biodegradability
  • Expedite new product development by screening out undesirable candidates virtually

Identify risks and predict performance in processing

  • Characterize interactions with functionalized surfaces for purification via filtration and adsorption/desorption steps
  • Predict the effect of solvents on mixing and processability of end product formulations
  • Investigate the ability to scale to manufacturing-level processes
BlogHow L’Oreal uses digital simulation to explore sustainable product ingredients

How L’Oreal uses digital simulation to explore sustainable product ingredients

Platform in action

Experimenting with new formulations in silico allows scientists at L’Oreal to make confident decisions far more quickly than if they were testing numerous new potential formulations in a laboratory—a process that often takes years to generate usable data.

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Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Advancing the design and optimization of drug formulations with coarse-grained molecular simulations

Read case study

Molecular dynamics and coarse-grained simulations facilitate the design of new eco-friendly cosmetic formulations

Read case study

Computer-aided formulation development for small molecule drugs

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Address formulation challenges across industries

Pharmaceuticals

Design optimized drug formulations for effective drug release and delivery.

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Consumer packaged goods

Develop sustainable formulations for healthier food and beverage, better cosmetics, cleaning products and packaging materials.

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Specialty Chemicals

Deliver novel and new specialty chemicals for optimized performance of end products.

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Request a demo

Online certification courses

Level up your skill set in formulation modeling with our self-paced, hands-on online certification courses.

Consumer Packaged Goods Course
Consumer packaged goods course
Learn how to apply Schrödinger’ software to predict key properties of simple and complex material formulations with automated workflows and machine learning models.
Molecular Modeling for Materials Science: Pharmaceutical Formulations
Pharmaceutical formulations course
Learn how to apply Schrödinger software to understand the behaviors of active pharmaceutical ingredients (APIs) in your drug formulations.

Key Products

Learn more about the key computational technologies available to progress your research projects.

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

MS Penetrant Loading

Molecular dynamics (MD) modeling for predicting water loading and small molecule gas adsorption capacity of a condensed system

MS Maestro

Complete modeling environment for your materials discovery

Desmond

High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy

MS CG

Efficient coarse-grained (CG) molecular dynamics (MD) simulations for large systems over long time scales

OPLS4 & OPLS5 Force Field

A modern, comprehensive force field for accurate molecular simulations

MS Morph

Efficient modeling tool for organic crystal habit prediction

LiveDesign

Your complete digital molecular design lab

MS Transport

Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity, conductivity and diffusions of atoms and molecules

Training Tutorials

Cluster Analysis
View tutorial
Calculating surfactant tilt and electrostatic potential of a bilayer system
View tutorial
Crystal Morphology
View tutorial
Molecular dynamics simulations for active pharmaceutical ingredient (API) miscibility
View tutorial
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Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Shearing Friction Behaviour of Synthetic Polymers Compared to a Functionalized Polysaccharide on Biomimetic Surfaces: Models for the Prediction of Performance of Eco-designed Formulations

Coscia B.J. et al. Polym. Degrad. Phys. Chem. Chem. Phys., 2023, 25, 1768-1780

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Exploring the Effects of Wetting and Free Fatty Acid Deposition on an Atomistic Hair Fiber Surface Model Incorporating Keratin-Associated Protein 5-1

Sanders J.M. et al. ACS Appl. Langmuir 2023, 39, 15, 5263–5274

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Molecular-Level Examination of Amorphous Solid Dispersion Dissolution

Afzal A. et al. Mol. Pharmaceutics 2021, 18, 11, 3999–4014

Learn more

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Catalysis and Reactivity

CATALYSIS & REACTIVITY

Drive innovation in catalysis, reactivity, and degradation R&D with digital chemistry

Request a demo
Transform industrial chemical production with digital chemistry

Accelerate catalyst design and optimize chemical reactions at the atomic level

The need for new materials is urgent, driven by growing market demands and stringent environmental regulations. To meet these needs, scientists across industries are on the search for new catalytic and non-catalytic processes that help reduce energy requirements, eliminate unwanted byproducts, and improve the selectivity and reactivity of chemical reactions.

Schrödinger’s advanced computational tools offer solutions that accelerate the discovery of these next generation catalytic and non-catalytic processes through cutting-edge physics-based modeling, machine learning (ML), and collaborative informatics platform.

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Intuitive computational workflows designed by catalysis experts

Easy-to-use system builders for all material types
Powerful workflows for physics-based simulation, machine learning, and data analysis
Dedicated customer support and extensive training resources

How we can help you innovate

Speed time to market of new catalysts

  • Predict physico-chemical properties of new catalysts
  • Gain a molecular-level understanding of homogeneous and heterogeneous catalytic mechanisms
  • Enable indirect and direct catalyst design

Optimize reactivity in non-catalytic reactions

  • Elucidate the mechanisms of chemical reactions
  • Automatically predict the selectivity and activity of reactants
  • Evaluate thermal decomposition products

Homogeneous & heterogeneous catalyst design

Flyer
Accelerate the design of high-performance heterogeneous catalysts

Efficient computational solutions leveraging atomic-scale simulation, machine learning, and enterprise informatics for catalytic reactions using solid-state catalysts

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Flyer
Accelerate the design of high-performance homogeneous catalysts

Efficient, highly-automated solutions for computational molecular design of catalysts leveraging the combination of quantum mechanics, molecular dynamics, and machine learning

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Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Accessible and automated computational catalyst discovery and reactivity optimization

In this webinar, we will demonstrate how an end-user physics–AI platform removes barriers to entry, making this process accessible to both experts and non-experts while enabling seamless scalability.

Materials Science Webinar

Accelerating materials discovery with physics-informed AI/ML

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Materials Science Webinar

Accelerating chemical innovation with AI/ML: Breakthroughs across materials applications

In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications.

Materials Science Webinar

High-performance materials discovery: A decade of cloud-enabled breakthroughs

This talk will showcase how Schrödinger’s integrated materials science platform enables massive parallel screening and de novo design campaigns across diverse applications.

Materials Science Webinar

Purposeful simulation: Maximising impact in surface chemistry modelling

In this webinar, learn about a variety of atomistic models of surfaces and gain perspective on the underlying rationale, benefits and limitations of each.

Materials Science Webinar

Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform

In this webinar, we demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.

Life Science Webinar

Computational reactivity and catalysis for drug synthesis

In this webinar, will take an in-depth look at how computational modeling is transforming pharmaceutical synthesis.

Materials Science Webinar

AI/ML meets physics-based simulations: A new era in complex materials design

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices. 

Materials Science Webinar

Webinar Series: From Molecules to Materials Applications

In this webinar series, we present molecular modeling techniques and their transformative impact on Materials Science research using the Schrödinger Materials Science tools.

Materials Science Webinar

Computational Catalysis at Schrödinger

In this webinar, we highlight the digital simulation tools specifically for Catalysis & Reactivity.

Address materials, energy, and environmental challenges across industries with computational catalysis

Oil & Gas

Maximize yield and minimize waste of oilfield chemicals, reduce manufacturing cost and carbon footprint.

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Specialty Chemicals

Drive the innovation of new chemistry design and meet the global demand of specialty chemicals.

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Plastics

Deliver high-performance plastics with improved production, recyclability, and biodegradability.

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Semiconductor

Enable optimized thin film deposition and etch at the surface.

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Consumer Packaged Goods

Develop innovative ingredients for consumer goods.

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Pharmaceuticals

Develop and produce innovative pharmaceutical ingredients for effective drug formulations.

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Request a demo
Featured CourseOnline certification course: Level-up your skill set in catalysis modeling

Molecular modeling for materials science applications: Homogeneous catalysis and reactivity course

Online certification course: Level-up your skill set in catalysis modeling

Learn how to apply industry-leading computational software to  predict key properties of organic and organometallic compounds, determine transition state and generate reaction profiles with automated workflows and machine learning models.

  • Self-paced learning content
  • Hands-on access to Schrödinger software
  • Guided and independent case studies
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Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Catalytic Selectivity Through Microkinetic Modeling

Learn to analyze the selectivity of the catalytic oxidation of CO and H2 on a Pd(111) surface using Microkinetic Modeling (MKM) calculations.

Materials Science Tutorial

Locating Adsorption Sites on Surfaces

Learn how to locate adsorption sites on surfaces.

Materials Science Documentation

Machine Learning Force Fields

Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.

Materials Science Tutorial

Machine Learning Force Field

Learn how to use machine learning force field optimization methods to prepare and simulate various systems.

Materials Science Documentation

MS Reactivity

Automated workflows for design, optimization, and unsupervised mechanism discovery in molecular chemistry.

Materials Science Documentation

MS Microkinetics

An efficient tool for surface reaction kinetics.

Materials Science Documentation

Formulation ML

A machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties.

Materials Science Tutorial

Applied Machine Learning for Formulations

Learn to apply the Formulation Machine Learning Panel across a range of materials applications. This tutorial assumes that you have already completed the Machine Learning for Formulations tutorial.

Materials Science Tutorial

Introduction to Materials Science Maestro Tutorial

An introduction to Materials Science Maestro, covering basic navigation, an intro to building models and several of the key functionalities of the graphical user interface.

Materials Science Documentation

Materials Science Documentation

Comprehensive reference documentation covering materials science panels and workflows.

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Key Products

Learn more about the key computational technologies available to progress your research projects.

Virtual Cluster

Secure, scalable environment for running simulations on the cloud

MS Reactivity

Automated workflows for design, optimization, and unsupervised mechanism discovery in molecular chemistry

MacroModel

Versatile, full-featured molecular modeling program

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

AutoTS

Automatic workflow for locating transition states for elementary reactions

MS Informatics

Automated machine learning tools for materials science applications

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

LiveDesign

Your complete digital molecular design lab

Quantum ESPRESSO Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

A density functional theory study of keto-enol tautomerism in 1,2-cyclodiones: Substituent effects on reactivity and thermodynamic stability

Materials Science Publication

Catalytic Intermolecular Asymmetric [2π + 2σ] Cycloadditions of Bicyclo[1.1.0]butanes: Practical Synthesis of Enantioenriched Highly Substituted Bicyclo[2.1.1]hexanes

Materials Science Publication

Olefination with sulfonyl halides and esters:Mechanistic DFT and experimental studies, andcomparison with reactivity of phosphonates

Materials Science Publication

Photooxygenation reactions under flow conditions: An experimental and in-silico study

Materials Science Publication

Cu-TiO2/Zeolite/PMMA Tablets for Efficient Dye Removal: A Study of Photocatalytic Water Purification

Materials Science Publication

Machine learning-based design of pincer catalysts for polymerization reaction

Materials Science Publication

Quantum chemical package Jaguar: A survey of recent developments and unique features

Materials Science Publication

Structure of methylaluminoxane (MAO): Extractable [Al(CH3)2]+ for precatalyst activation

Materials Science Publication

Unraveling the mechanisms underlying lignin and xylan dissolution in recyclable biphasic catalytic systems

Materials Science Publication

Probing the photostability of avobenzone with N-acetylcysteine using UV spectroscopy, computational studies and integration into aloe vera gel

View more publications

Schedule a consultation on Schrödinger’s catalysis and reactivity solutions.

Contact us today to explore how you can leverage advanced simulation and AI/ML for catalysis and reactivity.

Don’t see your areas of interest above? Reach out so we can help.

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Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Drug Formulation

 Drug Formulation

Deliver better medicines through in silico design

Request a demo
Optimize Drug Formulation Process

Optimize your drug formulation process with structure-based insights and efficient screening of formulation parameters

A smart, strategic drug formulation can efficiently advance your drug development projects and inform downstream processes. Advances in molecular modeling and machine learning are enabling atomistic-level insights to improve drug formulations and the ability to evaluate large numbers of candidate materials and formulations prior to experiments.

Schrödinger offers a range of computational solutions for advancing small molecule formulation, from crystalline or amorphous forms to selection of materials and excipients for processing, stability, and delivery.

Key Capabilities

Identify the most stable crystal polymorph to derisk your drug formulation

  • Efficiently predict the most stable crystal forms, starting from the 2D structures of the drug candidates, and generate thermodynamic stability ranking of different structures
  • Proactively identify alternative low energy crystal structures and avoid polymorphic transformation during development, manufacturing, and storage

Predict solubilities of drug candidates

  • Accurately predict solubility of amorphous and crystalline forms to encourage the discovery of a soluble API and to delineate the potential solubility boost from non-crystalline forms using FEP+
  • Identify instances where pure drug solubility can exceed the expected solubility due to the formation of small drug aggregates

Understand drug stability and reactivity

  • Predict glass transition temperature and water uptake in amorphous materials, including amorphous solid dispersions
  • Evaluate drug stability with respect to various degradation channels
  • Calculate bond dissociation energy to evaluate chemical stability

Characterize and optimize drug formulations and delivery

  • Gain insight into the complex requirements and behaviors of lipid-based and polymer-based formulations, including amorphous solid dispersions
  • Evaluate the impact of different polymers or polymer residues on the release solubilization and aggregation of the API
  • Predict key properties such as miscibility of ingredients, molecular interactions in solution, and drug release profiles

Optimize drug process development and manufacturing

  • Predict crystal morphology to anticipate powder flow challenges
  • Calculate Young’s and shear moduli to aid in the optimization of tableting conditions
  • Understand solubility in non-aqueous solvents
BlogTackling Drug Solubility: AbbVie and Schrödinger Collaborate to Advance Accurate Prediction Methods (FEP)

Tackling drug solubility: AbbVie and Schrödinger collaborate to advance accurate prediction methods

Despite its critical importance, early assessment of crystalline thermodynamic solubility continues to be elusive for drug discovery and development. Scientists from AbbVie and Schrödinger have been collaborating to explore new physics-based methods for solubility prediction.

Read More
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Featured Webinar

  • Life Science
  • Webinar

Molecular-level insight into solubility-enhancement via cosolvents and amorphous solid dispersions

Molecular-level insight into solubility-enhancement via cosolvents and amorphous solid dispersions
Watch webinar
View all drug formulation webinars
Featured courseMolecular Modeling for Materials Science: Pharmaceutical Formulations

Learn in silico drug formulation methods with our hands-on online certification course

Level-up your skills by enrolling in our online course, Molecular Modeling for Materials Science: Pharmaceutical Formulations.

Learn More

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Life Science Documentation

Crystal Structure Prediction

The Crystal Structure Prediction Panel can be used to explore potential crystal polymorphs and rank them based on their energies.

View more tutorials

Key Products

Learn more about the key computational technologies available to progress your research projects.

FEP+

High-performance free energy calculations for drug discovery

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

Desmond

High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy

MS Morph

Efficient modeling tool for organic crystal habit prediction

MS Maestro

Complete modeling environment for your materials discovery

MS CG

Efficient coarse-grained (CG) molecular dynamics (MD) simulations for large systems over long time scales

Crystal Structure Prediction

De-risk your solid form selection process by identifying the most stable polymorph at room temperature

Case studies & webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Accelerating amorphous solid dispersion (ASD) formulation with Schrödinger’s Materials Science Suite

This session will demonstrate how to seamlessly integrate computational insights from mixing energies to glass transition temperatures (Tg) into your existing R&D pipeline to reduce experimental iteration and accelerate time-to-market.

Materials Science Webinar

Formulation ML and Optimization: Making advanced property prediction and experimental design fast and accessible

Join our upcoming webinar to learn how your R&D organization can remove adoption barriers, accelerate discovery cycles, and align with national AI initiatives.

Materials Science Webinar

Formulation ML and Optimization: Making advanced property prediction and experimental design fast and accessible

We will showcase how easy it is to apply these tools using experimental datasets across broad MS applications, including formulations, consumer goods, batteries, pharmaceuticals, and beyond.

Materials Science Webinar

Formulation ML and Optimization: Making advanced property prediction and experimental design fast and accessible

We will showcase how easy it is to apply these tools using experimental datasets across broad MS applications, including formulations, consumer goods, batteries, pharmaceuticals, and beyond.

Materials Science Webinar

「Formulation MLとFormulation ML Optimization：高度な物性予測と実験計画を高速かつ身近なものに　ーAI駆動型マテリアルズ・ディスカバリーの加速」

AIを活用したマテリアルズ・ディスカバリー（材料探索）は、もはや実験的な取り組みではなく、国家レベルの新たなスタンダードとして定着しつつあります。

Materials Science Webinar

難溶性薬物の放出メカニズムを解明する – ASD研究の新たなアプローチModelling amorphous solid dispersion (ASD) release mechanisms

AbbVie と Schrödinger のエキスパートが、ASDにおける薬物放出やLoss of Release のメカニズムを、熱力学モデリング・分子シミュレーション・実験研究 を組み合わせた最新の研究成果を基に解説します。

Life Science Webinar

Computational tools for PROTAC design and optimization

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Life Science Webinar

Transforming small molecule drug discovery: The computational chemistry paradigm

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Life Science Webinar

Integrating physics-based insights to accelerate biologics design

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Life Science Webinar

Accelerating pharmaceutical formulations development: A computational approach

This webinar series will explore how cutting-edge computational methods are revolutionizing the design and optimization of pharmaceutical drugs, biologics , and advanced materials.

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Life Science Publication

A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study

Life Science Publication

Calculating apparent pKa values of ionizable lipids in lipid nanoparticles

Life Science Publication

Predicting the Release Mechanism of Amorphous Solid Dispersions: A Combination of Thermodynamic Modeling and In Silico Molecular Simulation

Life Science Publication

Coarse-grained simulation of mRNA-loaded lipid nanoparticle self-assembly

Life Science Publication

Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors

Life Science Publication

Free Energy Perturbation Approach for Accurate Crystalline Aqueous Solubility Predictions

Life Science Publication

On Ternary Complex Stability in Protein Degradation: In Silico Molecular Glue Binding Affinity Calculations

Life Science Publication

Intense bitterness of molecules: Machine learning for expediting drug discovery

View more publications

Software and services to meet your organizational needs

Software Platform

Deploy digital drug discovery workflows using a comprehensive and user-friendly platform for molecular modeling, design, and collaboration.

Research Services

Leverage Schrödinger’s computational expertise and technology at scale to advance your projects through key stages in the drug discovery process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Course bundle

Molecular modeling for materials science applications: course bundle
Watch Video 1m 08s

Course bundle


Includes access to all paid, materials science courses such as, Organic Electronics, Catalysis, Surface Chemistry, Formulations, Battery Materials and more! Note: The PyMOL course is excluded

Register Now
Details
Modules
30+
Duration
6 weeks / ~100 hours to complete
Level
Introductory, Bundle
Cost
$1510 for non-student users
$320 for student / post-doc
Course Timeframe
When registering for the course, you will be able to choose your preferred start and end date. Within those dates, you will have asynchronous access to the course to work on your preferred schedule

Overview

Computational molecular modeling tools have proven effective in materials science research and development. Chemists, physicists and engineers working in materials science will increasingly encounter molecular modeling throughout their careers, making it critical to have a foundational understanding of the cutting edge tools and methods.

This course is ideal for those who wish to develop professionally and expand their CV by earning certification and a badge. These computational chemistry courses offer an effective and efficient approach to learn practical computational chemistry for materials science:

  • Work hands-on with Schrödinger’s industry-leading Materials Science Maestro software
  • Jump start your research program by learning methods that can be directly applied to ongoing projects
  • Learn multi-scale approaches for materials design
  • Perform a completely independent case study to demonstrate mastery of the course content
  • Benefit from review and feedback from Schrödinger Education Team experts for course assignments and course-related queries
  • Work on the course materials on your own schedule whenever convenient for you

 

This course comes with access to a web-based version of Schrödinger software with the necessary licenses and compute resources for the course:

Requirements
  • A computer with reliable high speed internet access (8 Mbps or better)
  • A mouse and/or external monitor (recommended but not required)
  • Working knowledge of general chemistry
Certification
  • A certificate signed by the Schrödinger course lead
  • A badge that can be posted to social media, such as LinkedIn
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What you will learn

MS Maestro interface

Learn how to use an industry-leading interface for materials science modeling. No coding or scripting required to run modeling workflows

Quantum mechanics

Learn to apply molecular and periodic density functional theory (DFT) for automated property prediction for organic and inorganic systems

Molecular dynamics

Learn to leverage all-atom and coarse-grained molecular dynamics simulations for simulating soft matter and complex formulations

Machine learning

Learn to apply machine learning for rapid and accurate property prediction

Register Nowpreview course interface

Course syllabus

The course bundle includes access to the following seven courses in their entirety during the single course session.

Organic Electronics
Homogeneous Catalysis & Reactivity
Surface Chemistry
Battery Materials
Pharmaceutical Formulations
Polymeric Materials
Consumer Packaged Goods
Self-paced video lessons on materials modeling

Self-paced video lessons on materials modeling

Videos on practical theory break down complex scientific concepts (e.g. Molecular Quantum Mechanics)

Videos on practical theory break down complex scientific concepts (e.g. Molecular Quantum Mechanics)

Access cloud-based computing resources to perform calculations yourself

Access cloud-based computing resources to perform calculations yourself

Hands-on step-by-step tutorials (e.g. Pharmaceutical Formulations course, pKa prediction)

Hands-on step-by-step tutorials (e.g. Pharmaceutical Formulations course, pKa prediction)

Hands-on modeling in the web-based graphical user interface (e.g. Polymeric Materials course, Diffusion tutorial)

Hands-on modeling in the web-based graphical user interface (e.g. Polymeric Materials course, Diffusion tutorial)

Videos on practical theory break down complex scientific concepts (e.g. Molecular Dynamics)

Videos on practical theory break down complex scientific concepts (e.g. Molecular Dynamics)

On-demand video lessons on materials modeling

On-demand video lessons on materials modeling

Access cloud-based computing resources to perform calculations yourself

Access cloud-based computing resources to perform calculations yourself

Perform case studies with expert feedback (e.g. Organic Electronic Course, Independent Case Study)

Perform case studies with expert feedback (e.g. Organic Electronic Course, Independent Case Study)

Video on practical theory break down complex scientific concepts (e.g. Machine Learning for Chemistry)

Video on practical theory break down complex scientific concepts (e.g. Machine Learning for Chemistry)

Videos on practical theory break down complex scientific concepts (e.g. Periodic Quantum Mechanics)

Videos on practical theory break down complex scientific concepts (e.g. Periodic Quantum Mechanics)

Videos on practical theory break down complex scientific concepts (e.g. Coarse-Graining)

Videos on practical theory break down complex scientific concepts (e.g. Coarse-Graining)

Self-paced video lessons on materials modeling
Videos on practical theory break down complex scientific concepts (e.g. Molecular Quantum Mechanics)
Access cloud-based computing resources to perform calculations yourself
Hands-on step-by-step tutorials (e.g. Pharmaceutical Formulations course, pKa prediction)
Hands-on modeling in the web-based graphical user interface (e.g. Polymeric Materials course, Diffusion tutorial)
Videos on practical theory break down complex scientific concepts (e.g. Molecular Dynamics)
On-demand video lessons on materials modeling
Access cloud-based computing resources to perform calculations yourself
Perform case studies with expert feedback (e.g. Organic Electronic Course, Independent Case Study)
Video on practical theory break down complex scientific concepts (e.g. Machine Learning for Chemistry)
Videos on practical theory break down complex scientific concepts (e.g. Periodic Quantum Mechanics)
Videos on practical theory break down complex scientific concepts (e.g. Coarse-Graining)

Need help obtaining funding for a Schrödinger Online Course?

We proudly support the next generation of scientists and are committed to providing opportunities to those with limited resources. Learn about your funding options for our online certification courses as a student, post-doc, or industry scientist and enroll today!

Scholarship programFunding options

What our alumni say

“Clear instructions with a well-designed interface allowed me to run some of my own first molecular dynamics simulations. The information from the course felt much more secure than the information from YouTube because I knew it was developed by experts”
Graduate Student
“The course let me talk confidentially about molecular modeling and what it can do. For me, this was a nice experience which left me with many ideas for applying molecular modeling in the research area of our department, not only for me but also for my colleagues.”
Graduate Student
“As always, the course is very well designed. Formulation is quite outside my comfort zone in terms of theory and modeling but this course provided me with knowledge of evaluating what modeling can facilitate in the real world. Really great design and education process.”
Senior DirectorTherapeutic Protein Design

Show off your newly acquired skills with a course badge and certificate

When you complete a course with us in molecular modeling and are ready to share what you learned with your colleagues and employers, you can share your certificate and badge on your LinkedIn profile.

Frequently asked questions

How much do the online courses cost?

Pricing varies by each course and by the participant type. For students wishing to take these courses, we offer a student price of $150 for introductory courses, $305 for the Materials Science bundle, and $870 for advanced courses. For commercial participants, the course price is $575 for introductory courses and $1435 for advanced courses and bundles.

When does the course start?

The courses run on sessions, which range from 3-6 week periods during which the course and access to software are available to participants. You can find the course session and start dates on each course page.

What time are the lectures?

Once the course session begins, all lectures are asynchronous and you can view the self-paced videos, tutorials, and assignments at your convenience.

How could I pay for this course?

Interested participants can pay for the course by completing their registration and using the credit card portal for an instant sign up. Please note that a credit card is required as we do not accept debit cards. Additionally, we can provide a purchase order upon request, please email online-learning@schrodinger.com if you are interested in this option. If you have any questions regarding how to pay for the course, please visit our funding options page.

Materials Science CoursesFunding options
How can I preview the course before registering?
Free Course Preview
Are there any scholarship opportunities available for students?

Schrödinger is committed to supporting students with limited resources. Schrödinger’s mission is to improve human health and quality of life by transforming the way therapeutics and materials are discovered. Schrödinger proudly supports the next generation of scientists. We have created a scholarship program that is open to full-time students or post-docs to students who can demonstrate financial need, and have a statement of support from the academic advisor. Please complete the application form if you qualify for our scholarship program!

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Will material still be available after a course ends?

While access to the software will end when the course closes, some of the material within the course (slides, papers, and tutorials) are available for download so that you can refer back to it after the course. Other materials, such as videos, quizzes, and access to the software, will only be available for the duration of the course.

Do I need access to the software to be able to do the course? Do I have to purchase the software separately?

For the duration of the course, you will have access to a web-based version of Maestro, Bioluminate, Materials Science Maestro and/or LiveDesign (depending on the course). You do not have to separately purchase access to any software. While access to the software will end when the course closes, some of the material within the course (slides, papers, and tutorials) are available for download so that you can refer back to it after the course. Other materials, such as videos, quizzes, and access to the software, will only be available for the duration of the course. Please note that Schrödinger software is only to be used for course-related purposes.

Related courses

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All-atom molecular dynamics and machine learning approaches for studying polymeric materials and their properties under various conditions

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Molecular quantum mechanics, periodic quantum mechanics, and machine learning approaches for studying atomic layer processing and heterogeneous catalysis

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Supporting associations

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GA Optoelectronics

GA Optoelectronics

Design solution for novel molecular materials in optoelectronic applications based on a generative algorithm

Request a demoView Documentation
GA Optoelectronics

Overview

GA Optoelectronics enables the generation of novel chemical space for molecular materials with desired properties for optoelectronic applications via a genetic algorithm. The optoelectronics capabilities are designed to leverage rapid screening to complement experimental development by elucidating molecular properties and informing future synthetic targets.

Key Capabilities

Generate a library of compounds within the target property space with genetic optimization
Account for multiple properties when optimizing molecular structures (e.g redox potential, excited state energies or any experimental properties)
Design new and diverse structures using density functional theory (DFT) calculations or pre-built machine learning models
Explore structural mutations (element, fragment, isoelectronic) within basic molecular design constraints (number of atoms, molecular weight)

Broad applications across materials science research areas

Get more from your ideas by harnessing the power of large-scale chemical exploration and accurate in silico molecular prediction.

Polymeric Materials
Pharmaceutical Formulations & Delivery
Energy Capture & Storage
Organic Electronics
Consumer Packaged Goods
Catalysis & Reactivity

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Documentation

GA Optoelectronics

A design solution for novel molecular materials in optoelectronic applications based on a generative algorithm.

Materials Science Tutorial

Genetic Optimization

Generate new structures for which a chosen set of optoelectronic properties is optimized by mutating the structures with a genetic algorithm.

Materials Science Tutorial

Optoelectronics Active Learning

Learn to predict optoelectronic properties using active learning models for a series of iridium complexes.

View more tutorials

Related Products

Learn more about the related computational technologies available to progress your research projects.

MS Maestro

Complete modeling environment for your materials discovery

MS Informatics

Automated machine learning tools for materials science applications

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

n-Type naphthalimide-indole derivative for electronic applications

Materials Science Publication

Design of organic electronic materials with a goal-directed generative model powered by deep neural networks and high-throughput molecular simulations

Materials Science Publication

Achieving High Efficiency and Pure Blue Color in Hyperfluorescence Organic Light Emitting Diodes using Organo-Boron Based Emitters

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Rapid Multiscale Computational Screening for OLED Host Materials

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Atomic-scale Simulation for the Analysis, Optimization and Accelerated Development of Organic Optoelectronic Materials

Materials Science Publication

Virtual Screening for OLED Materials

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Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Learn More

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.

Learn More
Other Resources

MS Maestro

MS Maestro

Complete modeling environment for your materials discovery

Request a demoView Documentation
Materials Science: Maestro

Discover better materials, faster

MS Maestro is a streamlined interface for atomic-scale structural visualization, cutting-edge physics-based computational modeling, and machine learning workflows for materials discovery and analysis. MS Maestro provides insights into the mechanisms and properties of materials and chemical systems in a wide range of technological applications such as catalysis, polymers, batteries, consumer packaged goods, renewable energy, and semiconductors to accelerate materials innovation.

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Supercharge materials discovery with an integrated molecular modeling platform

Unified molecular modeling environment

  • Access integrated workflows and analysis tools with automated simulations powered by quantum mechanics (QM), molecular dynamics (MD), and molecular mechanics (MM)
  • Benefit from pre-configured and customizable workflows for performing best-in-class molecular simulations

Portal to state-of-the art machine learning workflows

  • Take advantage of advanced machine learning and informatics tools for chemistry
  • Access pre-built machine learning models for predicting key materials properties
  • Automate model building and validation processes with the support of wide feature space and a variety of regression methods

Intuitive, full-featured structure builders

  • Build realistic structural and system models of any materials type, including crystals, organometallic complexes, polymers, surfaces, interfaces, and more
  • Quickly and accurately render large-scale, complex materials models in 3D workspace

Intuitive chemical enumeration capabilities

  • Generate and store chemical structures through structural enumeration using advanced combinatorial chemistry tools
  • Build and manage large-scale chemical libraries with graphical user interface

Remote job management & cross-platform support

  • Manage large-scale computational modeling and simulation tasks on local/remote compute servers across Linux, Windows, Mac, and cloud
  • Make Maestro accessible to teams in a secure, virtual cloud environment
Integrated with LiveDesign for collaboration & model deployment

Integrated with LiveDesign for collaboration & model deployment

  • Crowdsource ideas and interactively revise design strategies with your colleagues — anytime, anywhere
  • Break down data silos and gain real-time access to all project data — virtual and experimental — in a single centralized platform

Case Studies & Webinars

Discover how Schrödinger technology is being used to solve real-world research challenges.

Materials Science Webinar

Taking experimentation digital: Materials innovation using atomistic simulation and machine learning at-scale

In this webinar, we introduce a modern approach to materials R&D using a digital chemistry platform for in silico analysis, optimization and discovery.

Materials Science Webinar

In silico materials development: Integrating atomistic simulation into academic chemistry and engineering labs

In this webinar, we explore Schrödinger’s leading physics-based and machine learning computational technologies and provide a comprehensive introduction to the capabilities of computational modeling in chemistry, materials science, and engineering.

Materials Science Webinar

Quick Start Workshop: Materials Simulation for Experimentalists

In this webinar, learn how an experimentalist can take advantage of simulation and modeling, as well as practical knowledge about how to get started.

Materials Science Webinar

A chemist’s view on R&D digitalization

In this webinar, we illustrate how the integration of Schrödinger’s machine learning technologies with physics based modelling can be utilized to predict properties of new materials.

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Panel discussion: Materials design at scale

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Perspectives in Computational Materials Design: Progress and Prospects

In this webinar, we present new strategies for multiparadigm simulations of nanoscale materials with applications to electrocatalysis, Li batteries, micelle formation, and ductile boron carbide.

Schrödinger Suite Release 2023-4

Materials Science Product Guide

Explore our complete guide to Schrödinger’s Materials Science products

Download guide

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Catalytic Selectivity Through Microkinetic Modeling

Learn to analyze the selectivity of the catalytic oxidation of CO and H2 on a Pd(111) surface using Microkinetic Modeling (MKM) calculations.

Materials Science Tutorial

Ionic Conductivity

Learn to calculate the ionic conductivity.

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Locating Adsorption Sites on Surfaces

Learn how to locate adsorption sites on surfaces.

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Simulating Complex Protein Solutions

Learn to prepare a complex protein system for a Molecular Dynamics (MD) simulation.

Materials Science Tutorial

Creating a Coarse-Grained Model for Protein Formulations

Learn to use the Coarse-Grained Force Field Builder to automatically fit parameters to the Martini coarse-grained force field for a complex protein solution system.

Materials Science Tutorial

Optimizing Viscosity and Cost in Formulations with Missing Structural Data

Learn to build a machine learning (ML) model to predict cost and viscosity of shampoo formulations with missing structural data.

Materials Science Tutorial

Building and Analyzing a Complex Lipid Bilayer and Embedding a Membrane Protein

Learn to build and analyze a complex lipid bilayer and how to embedd a protein.

Materials Science Tutorial

Computational Ellipsometry

Learn how to compute the refractive index and extinction coefficient of systems of organic optoelectronics.

Materials Science Documentation

Machine Learning Force Fields

Machine Learning Force Fields (MLFFs) offer a novel approach for predicting the energies of arbitrary systems.

Materials Science Tutorial

Machine Learning Force Field

Learn how to use machine learning force field optimization methods to prepare and simulate various systems.

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Broad applications across materials science research areas

MS Maestro provides a unified entry point for discovering molecular insights and accessing integrated solutions for:

Polymeric Materials
Pharmaceutical Formulations & Delivery
Energy Capture & Storage
Organic Electronics
Thin Film Processing
Catalysis & Reactivity
Metals, Alloys & Ceramics

Software and services built for your needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Learn More

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.

Learn More
Other Resources