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 models for quantitative structure-property relationships (QSPR) for single-molecule predictions
Generate advanced 2D, 3D, and repeat-unit-based descriptors for organic, inorganic, and polymeric materials
Featurize materials using quantum mechanics (QM), semiempirical QM molecular, and molecular dynamics (MD) descriptors
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
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
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.
AI/ML meets physics-based simulations: A new era in complex materials design
Advanced Machine Learning and Molecular Simulations for Formulation Design
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
Machine-learned force fields for improved materials modeling
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
DeepAutoQSAR
Automated, scalable solution for the training and application of predictive machine learning models
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.
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.
Other Resources