Accelerating Catalysis and Reactivity R&D with Atomic-scale Simulation, Machine Learning, and Enterprise Informatics

As market demands evolve, R&D scientists across industries — from consumer packaged goods to automotives — face similar challenges in developing and optimizing the next-generation of catalysts. Scientists need new catalysts that help them reduce energy requirements, eliminate unwanted side products, and improve the selectivity and reactivity of reactions.

Solution Overview

We need new materials, and we need them fast. Fortunately, the continual march of scientific progress promises faster answers than in the past. Computer-driven molecular design, with its ability to generate massive quantities of simulated data, facilitates entry into new frontiers of chemical discovery for sustainable materials design. It brings the promise of speed and accuracy, and it allows R&D scientists to scan through large molecular space to triage down and experimentally test only the most promising chemistries.

Schrödinger’s Materials Science platform leverages the power of physics-based simulation, machine learning, and enterprise informatics to enable the optimization and discovery of effective and selective catalysts and reactive systems by offering:

Differentiated model builders
Interactive visualization and analysis tools
Highly-efficient density functional theory (DFT) engines
Customizable automated reaction workflows
A collaborative enterprise platform solution

Application Overview

Catalysts

Advance the knowledge of catalytic mechanisms: Simulate catalytic pathways to fundamentally understand the forces providing reactivity and selectivity
Improve the lifetime of catalysts: Predict stability and degradation of catalysts and products
Design and optimize catalysts: Automatically run high-throughput screening of catalysts by leveraging known mechanistic pathways with novel catalysts
Benefit from inherently chemically agnostic workflows: Accelerate and automate any reaction mechanism in both homogeneous and heterogeneous screening workflows

Reactive Systems

Understand reaction mechanism and pathways: Simulate reaction thermodynamics, kinetics, reaction rates, and barriers
Accelerate product development: Automatically predict selectivity and activity of reactions with a library of interesting substrates and catalysts
Optimize product properties: Compute tacticity of polymers with relative reaction pathway energies
Predict product distribution: Automatically locate transition state
Drive innovation in reaction design: Study effect of steric, electronic, and ligand on the reactivity

Team Collaboration and Digital Data Management

Empower team collaboration: Employ webbased enterprise informatics tools for sharing experimental and predictive models seamlessly
Amplify research with improved decisionmaking: Rapidly deploy automated reaction workflows and machine learning models to drive large-scale predictions and assist novel design approaches
Improve project management: Accelerate project communication and collective learning by capturing, analyzing, and testing new ideas and data in a centralized platform