MS Informatics

Automated machine learning tools for materials science applications

Materials Science Informatics

Overview

MS Informatics provides molecular featurization and machine learning (ML) tools for organic, organometallic, polymer, chemical mixture (i.e., formulation), and inorganic solid materials to help design new materials through data driven approaches. By combining physics-based featurization, customized pretrained ML models, and automated workflows to train and evaluate ML models, users can take advantage of the computationally efficient data-driven approaches to screen and down select promising materials.

Key Capabilities

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Build models for quantitative structure-property relationships (QSPR) for single-molecule predictions
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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 using quantum mechanics (QM), semiempirical QM molecular, and molecular dynamics (MD) descriptors
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Build formulation-property models for chemical mixtures with varying ingredient structures and compositions

Includes pretrained 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 of small molecules
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Viscosity of small molecules
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Aqueous solubility of organic molecules
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Optoelectronic properties

Absorption and emission peak position and bandwidth (FWHM)
Extinction coefficient
Emission lifetime
Photoluminescence quantum yield (PLQY)
Singlet-triplet energy gap (S1-T1)
Oxidation and reduction potentials

Case studies & webinars

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

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
Pharmaceutical Formulations & Delivery
Consumer Packaged Goods

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Tutorial

Machine Learning for Formulations

Learn to build and apply machine learning models to predict the density of multicomponent mixtures.

Materials Science Tutorial

Periodic Descriptors for Inorganic Solids

Generate descriptors for inorganic periodic crystal systems which can be used to build machine learning models.

Materials Science Tutorial

Polymer Descriptors for Machine Learning

Generate descriptors for polymers which can be used to build machine learning models.

Materials Science Tutorial

Molecular Dynamics Descriptors for Machine Learning

Generate descriptors using molecular dynamics simulation, which can be used to build machine learning models.

Materials Science Tutorial

Machine Learning for Ionic Conductivity

Generate descriptors for ionic liquids which can be used to build machine learning models.

Materials Science Tutorial

Machine Learning for Sweetness

Learn to use the DeepAutoQSAR panel to predict whether a molecule is sweet by machine learning methods.

Materials Science Tutorial

Cheminformatics Machine Learning for Homogeneous Catalysis

Learn to develop and use a machine learning model to predict reaction rate constants for iridium catalysts.

Materials Science Tutorial

Machine Learning Property Prediction

Learn to use pre-built machine learning models to predict polymer properties and volatility for organic and organometallic molecules.

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 Interface

Integrated graphical user interface for nanoscale quantum mechanical simulations

Publications

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

Materials Science

Designing the Next Generation of Polymers with Machine Learning and Physics-Based Models

Materials Science

Machine learning-based design of pincer catalysts for polymerization reaction

Materials Science

Conformers influence on UV-absorbance of avobenzone

Materials Science

Leveraging High-throughput Molecular Simulations and Machine Learning for Formulation Design

Materials Science

Synthesis, computational studies and evaluation of benzisoxazole tethered 1,2,4-triazoles as anticancer and antimicrobial agents

Materials Science

Leveraging High-throughput Molecular Simulations and Machine Learning for Formulation Design

Materials Science

Unveiling a Novel Solvatomorphism of Anti-inflammatory Flufenamic Acid: X-ray Structure, Quantum Chemical, and In Silico Studies

Materials Science

Modified t-butyl in tetradentate platinum (II) complexes enables exceptional lifetime for blue-phosphorescent organic light-emitting diodes

Materials Science

Insights into the binding mechanism of 2,5-substituted 4-pyrone derivatives as therapeutic agents for fused dimeric interactions: A computational study using QTAIM, dynamics and docking simulations of protein–ligand complexes

Materials Science

Self-Assembled Tamoxifen-Selective Fluorescent Nanomaterials Driven by Molecular Structural Similarity

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.