Energy capture & storage

Develop cleaner, safer energy materials with digital chemistry

Develop cleaner, safer energy materials with digital chemistry
Discover and optimize energy materials at the molecular level

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

Case studies & webinars

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

Application note: Battery and energy storage materials

Energy capture and storage

Leveraging atomic scale modeling for design and discovery of next-generation battery materials

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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Key products

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


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

MS Informatics

Efficient machine learning model builder for materials science applications

MS Maestro

Complete modeling environment for your materials discovery


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


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 Transport

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


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


Integrated graphical user interface for nanoscale quantum mechanical simulations

Training Tutorials

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


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

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

Elementary decomposition mechanisms of lithium hexafluorophosphate in battery electrolytes and interphases

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

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.