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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

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Easy-to-use system builders for all material types
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Powerful workflows for physics-based simulation, machine learning, and data analysis
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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

Designing the next generation of lithium-ion batteries using a computational toolbox Blog
Designing the next generation of lithium-Ion batteries using a computational toolbox
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Combating climate change with next-generation batteries Blog
Combating climate change with next-generation batteries
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Case studies & webinars

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

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

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.

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

Efficient Computation of Process Parameters for Controlling the Chemistry of Deposition or Etch

In this webinar, we illustrate how atomic-scale DFT can be embedded into higher-level computational schemes for accurate and achievable prediction of the conditions and parameters for controlling chemical processes.

Materials Science Webinar

Harnessing Molecular Modeling to transform innovation in Polymeric Materials and Consumer Packaged Goods

In this webinar, we highlight Schrödinger’s Materials Science tools that can accelerate R&D efforts in these scientific domains.

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

Molecular Modeling: A Key to Solving Real-Life Challenges in Pharma Formulations

In this webinar, we describe how the demand for innovative drug delivery methods has driven researchers to explore the intricate structure-property relationships within pharmaceutical formulations.

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 Documentation

Quantum ESPRESSO Interface

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

Materials Science Tutorial

Optimization of Formulations Using Machine Learning

Learn to build machine learning (ML) models to predict distinct properties of formulations and leverage these models to optimize formulations for desired target properties.

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.

Life Science Tutorial

Ab initio Molecular Dynamics Simulations of Li-ion Diffusion in Solid State Electrolytes

Learn to perform an ab initio molecular dynamics simulation and calculate the Li-ion diffusion in a solid state electrolyte.

Life Science Tutorial

Phase Diagrams

Plot phase diagrams for a two- and three-component system.

Life Science Tutorial

Defect Energy Calculation

Learn to generate the defect energy correction for MgO.

Materials Science Documentation

Materials Science Documentation

Comprehensive reference documentation covering materials science panels and workflows.

Materials Science Tutorial

Introduction to Geometry Optimizations, Functionals and Basis Sets

Perform geometry optimizations on simple organic molecules and learn basics regarding functionals and basis sets.

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.

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Key products

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

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 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

Training Tutorials

Liquid electrolyte properties: Part 1
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Liquid electrolyte properties: Part 2
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Calculating voltage curves of spinel intercalation compounds
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Machine learning for ionic conductivity
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Publications

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

High-dimensional neural network potential for liquid electrolyte simulations

Dajnowicz S et al. J. Phys. Chem. B 2022, 126, 33, 6271–6280

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Data-driven discovery of small electroactive molecules for energy storage in aqueous redox flow batteries

Zhang Q et al. Energy Storage Materials, 2022, 47, 167-177

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Elementary decomposition mechanisms of lithium hexafluorophosphate in battery electrolytes and interphases

Persson K.A. et al. ACS Energy Lett. 2023, 8, 1, 347–355

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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.