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MS Reactivity

Automatic workflow for accurate prediction of reactivity and catalysis

Materials Science: Reactivity


MS Reactivity offers a set of automated tools to predict molecular reactivity, in particular molecular catalyst design. Identification of molecular catalysts capable of providing both high selectivities and reaction rates is a great challenge for modern homogeneous catalysis, traditionally relying on a long and expensive trial-and-error approach. MS Reactivity offers a digital approach for molecular catalyst design based on user-friendly Automated Reaction Workflow (AutoRXNWF).

The high-throughput AutoRXNWF represents the first-ever computational workflow that can predict both catalyst’s selectivity (regio-, chemo- and/or enantioselectivity) and/or turnover frequency (TOF) from quantum mechanics. The workflow offers optional conformational search and geometry pre-optimization with classical force fields and/or extended tight-binding (xTB) and runs (pseudospectral) density functional theory (DFT) at the last stage. Among various Boltzmann-averaged output properties, machine learning (ML) descriptors are also available on demand.

Automated Reaction Workflow Massively parallel physics-based computational workflow for molecular catalyst design graphic

Key Capabilities

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Automatically screen high-throughput reactions and catalysts to predict reactivity and selectivity, including additional energy and solvation corrections
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Compute reaction rates, barriers, and ML descriptors automatically during post-processing
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Identify energetic span, TOF calculations and determining states
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Identify the lowest energy structures with conformational sampling
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Optimize geometries, compute quantum energies, perform conformational ensemble and Boltzmann averaging 
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Visualize contour plots of buried volumes for catalysts 
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Remove structures with unwanted frequencies from post processing
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Perform anharmonic corrections to thermophysical properties
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Visualize reaction coordinate images with relative energies for all properties

Case studies & webinars

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

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

Broad applications across materials
science research areas

Get more from your ideas by harnessing the power of large-scale chemical exploration and accurate
in silico molecular prediction.

Catalysis & Reactivity
Polymeric Materials
Thin Film Processing
Organic Electronics
Energy Capture & Storage
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Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.


Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.