MS Informatics
Automated machine learning tools for materials science applications
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
Build accurate material-property relationships using computationally efficient machine learning models for organic molecules, polymers, formulations, and more
Enhance model predicability with advanced, physics-informed descriptors for organic, inorganic, and polymer materials using cheminformatics, semi-empirical approaches, quantum mechanics (QM), and molecular dynamics (MD)
Use pre-trained ML models to predict properties such as polymer glass transition temperature, viscosity, density, and a variety of optoelectronic properties for molecules
Interact through an intuitive GUI to perform single-point and geometry optimization using Schrödinger’s universal machine learning force field (MPNICE) for both periodic and gas-phase systems
Case studies & webinars
Discover how Schrödinger technology is being used to solve real-world research challenges.
Includes pretrained machine learning models to predict a diverse range of properties
Boiling point and vapor pressure of organic and organometallic compounds
Glass transition temperature of polymers
Frequency-dependent polymer dielectric constant and dielectric loss
Density of small molecules
Viscosity of small molecules
Aqueous solubility of organic molecules
Non-aqueous solubility
Melting point
HOMO/LUMO
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
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.
Related Products
Learn more about the related computational technologies available to progress your research projects.
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Publications
Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.
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.
What our alumni say