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MS Reactivity

Automatic workflows for accurate prediction of reactivity and catalysis

Materials Science: Reactivity

Overview

MS Reactivity offers two highly automated modules for modeling in molecular chemistry and catalysis. The first module, Automated Reaction Workflow (AutoRXNWF), is intended for chemical reaction and reactivity optimization with quantum mechanics based on a user-defined library and a reference reaction. One example of the application of AutoRXNWF is molecular catalyst design. The second module, Nanoreactor, is intended to identify reaction products for any reaction and sort them based on thermodynamics principles. This is achieved via automated potential energy surface (PES) sampling with semiempirical metadynamics, PES refinement, and sorting based on free energies. One of the applications of Nanoreactor is the study of small molecule degradation products without any prior knowledge.

AutoRXNWF

The high-throughput AutoRXNWF represents the first-ever computational workflow that can predict both a catalyst’s selectivity (regio-, chemo- and/or enantioselectivity) and 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 output properties, machine learning (ML) descriptors are also available on demand.

Automated reaction workflow.

Key Capabilities

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Run automated quantum-mechanics-based screening of reaction paths generated from a reference reaction and user defined r-group libraries
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Automatically compute selectivity, TOF, and identify energetic span and determining states
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Perform conformational sampling and Boltzmann averaging

Nanoreactor

Chemical degradation is the process by which chemical substances undergo structural changes, leading to the breakdown of their molecular integrity into simpler chemical compounds. This process is at the heart of chemical failure and material lifetime, natural degradation and aging, and recycling. It unfolds through diverse mechanisms, among which thermal decomposition, photolysis, oxidation, and hydrolysis are the most prevalent. With Schrödinger’s Nanoreactor, you can effortlessly discern all potential degradation products for small molecules and categorize them based on thermodynamic principles. Simply furnish the input structure and click the run button to unlock a comprehensive analysis.

Input (XYZ, SMILES) Potential Energy Surface sampling. Sorting.

Key Capabilities

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Automatically explore potential energy surface through semiempirical metadynamics, landscape refinement, and density functional theory-based sorting
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Predict reaction products without any prior knowledge
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Run simulations with minimal computational resources (e.g. on a laptop)

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

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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

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.

Tutorials

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.