MS Informatics
Automated machine learning tools for materials science applications
Automated machine learning tools for materials science applications
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.
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
Discover how Schrödinger technology is being used to solve real-world research challenges.
Get more from your ideas by harnessing the power of large-scale chemical exploration and accurate in silico molecular prediction.
Learn more about the related computational technologies available to progress your research projects.
Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties
Automated, scalable solution for the training and application of predictive machine learning models
Integrated graphical user interface for nanoscale quantum mechanical simulations
Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.
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.
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.