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

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
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
Advancing Material Property Prediction: Using Physics-Informed Machine Learning Models for Viscosity
Materials Science
Advancing material property prediction: using physics-informed machine learning models for viscosity
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.