- 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
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.
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
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 about the key computational technologies available to progress your research projects.
Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties
Efficient machine learning model builder for materials science applications
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
Modern, comprehensive force field for accurate molecular simulations
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
Quantum ESPRESSO GUI
Integrated graphical user interface for nanoscale quantum mechanical simulations
Software and services to meet your organizational needs
Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.
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.