MS Informatics

Efficient machine learning model builder for materials science applications

Materials Science Informatics

Overview

MS Informatics provides machine learning (ML) and featurization tools for organic and organometallic molecular materials, polymers, and inorganic solid materials to assist informatics-based materials research. By combining physics-based featurization with customized pre-built ML models, users can take advantage of speed of data-driven approaches in addition to the generalizability and accuracy of physics-driven methods.

Key Capabilities

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Analyze structures and diversity of a large chemical space with cheminformatics tool
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Generate advanced 2D, 3D, and repeat-unit-based descriptors for organic, inorganic, and polymeric materials
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Featurize materials  by quantum mechanics (QM) and semiempirical QM molecular descriptors
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Build models for quantitative structure-property relationships (QSPR)

Includes prebuilt machine learning models to predict a diverse range of properties

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Boiling point and vapor pressure of organic and organometallic compounds
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Glass transition temperature of polymers
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Frequency-dependent polymer dielectric constant and dielectric loss
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Density
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Viscosity

Case Studies

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

How machine learning enables accurate prediction of precursor volatility

Accelerating the design and optimization of OLED materials using active learning

De novo design of hole-conducting molecules for organic electronics

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
Energy Capture & Storage
Organic Electronics
Polymeric Materials
Complex Formulations
Consumer Packaged Goods

Related Products

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

Jaguar

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

MS Maestro

Complete modeling environment for your materials discovery

DeepAutoQSAR

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

AutoTS

Automatic workflow for locating transition states for elementary reactions

Quantum ESPRESSO GUI

Integrated graphical user interface for nanoscale quantum mechanical simulations

LiveDesign

Your complete digital molecular design lab

Publications

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

Materials Science
Development of Scalable and Generalizable Machine Learned Force Field for Polymers
Materials Science
Benchmarking Machine Learning Descriptors for Crystals
Materials Science
Quantitative structure-activity relationship(QSAR) models for color and COD removal for some dyes subjected to electrochemical oxidation
Materials Science
Design of organic electronic materials with a goal-directed generative model powered by deep neural networks and high-throughput molecular simulations
Materials Science
Bitter or not? BitterPredict, a tool for predicting taste from chemical structure
Materials Science
Virtual Screening for OLED Materials

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.