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Energy capture & storage

Develop cleaner, safer energy materials with digital chemistry

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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

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. 

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.

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
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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

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.

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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.