Drive innovation in catalysis, reactivity, and degradation R&D with digital chemistry

Transform industrial chemical production with digital chemistry

Accelerate catalyst design and optimize chemical reactions at the atomic level

The need for new materials is urgent, driven by growing market demands and stringent environmental regulations. To meet these needs, scientists across industries are on the search for new catalytic and non-catalytic processes that help reduce energy requirements, eliminate unwanted byproducts, and improve the selectivity and reactivity of chemical reactions.

Schrödinger’s advanced computational tools offer solutions that accelerate the discovery of these next generation catalytic and non-catalytic processes through cutting-edge physics-based modeling, machine learning (ML), and collaborative informatics platform.

background pattern

Intuitive computational workflows designed by catalysis 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

How we can help you innovate

Speed time to market of new catalysts

Speed time to market of new catalysts

  • Predict physico-chemical properties of new catalysts
  • Gain a molecular-level understanding of homogeneous and heterogeneous catalytic mechanisms
  • Enable indirect and direct catalyst design
Optimize reactivity in non-catalytic reactions

Optimize reactivity in non-catalytic reactions

  • Elucidate the mechanisms of chemical reactions
  • Automatically predict the selectivity and activity of reactants
  • Evaluate thermal decomposition products
dark theme background

Homogeneous & heterogeneous catalyst design

Accelerate the design of high-performance heterogeneous catalysts

Efficient computational solutions leveraging atomic-scale simulation, machine learning, and enterprise informatics for catalytic reactions using solid-state catalysts

Accelerate the design of high-performance homogeneous catalysts

Efficient, highly-automated solutions for computational molecular design of catalysts leveraging the combination of quantum mechanics, molecular dynamics, and machine learning

Case studies & webinars

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

How Novel Sustainable Catalysts Can Help Decarbonize Our Planet

An automated workflow for rapid large-scale computational screening to meet the demands of modern catalyst development

Accelerating the design of asymmetric catalysts with a digital chemistry platform

dark theme background

Address materials, energy, and environmental challenges across industries with computational catalysis

Oil & Gas

Maximize yield and minimize waste of oilfield chemicals, reduce manufacturing cost and carbon footprint.

Learn more
Specialty Chemicals

Drive the innovation of new chemistry design and meet the global demand of specialty chemicals.

Learn more

Deliver high-performance plastics with improved production, recyclability, and biodegradability.

Learn more

Enable optimized thin film deposition and etch at the surface.

Learn more
Consumer Packaged Goods

Develop innovative ingredients for consumer goods.

Learn more

Develop and produce innovative pharmaceutical ingredients for effective drug formulations.

Learn more
Featured CourseOnline certification course: Level-up your skill set in catalysis modeling

Molecular modeling for materials science applications: Homogeneous catalysis and reactivity course

Online certification course: Level-up your skill set in catalysis 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

Key Products

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

MS Reactivity

Automatic workflow for accurate prediction of reactivity and catalysis


Versatile, full-featured molecular modeling program

MS Maestro

Complete modeling environment for your materials discovery


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


Automatic workflow for locating transition states for elementary reactions

MS Informatics

Efficient machine learning model builder for materials science applications


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


Your complete digital molecular design lab


Integrated graphical user interface for nanoscale quantum mechanical simulations

Training Tutorials

Locating transition states: Part 1
View tutorial
Organometallic Complexes
View tutorial
Reaction workflow for polyethylene insertion
View tutorial
Cheminformatics machine learning for homogeneous catalysis
View tutorial


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

Iron-catalysed synthesis and chemical recycling of telechelic 1,3-enchained oligocyclobutanes

Chirik P. J. et al. Nature Chemistry 2021, 13, 156-162

Exploring the mechanism of Cr(VI) catalyzed hypochlorous acid decomposition

Busch M et al. ChemCatChem 2022, e202101850

Titanium Dioxide as the Most Used Photocatalyst for Water Purification: An Overview

Armaković S.J et al. Catalysts 2023, 13(1), 26

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.