background pattern

Computational Chemistry

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

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
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.

Learn More
dark theme background

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.