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MS Informatics

Automated machine learning tools for materials science applications

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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

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.

Materials Science Webinar

Formulation ML and Optimization: Making advanced property prediction and experimental design fast and accessible

We will showcase how easy it is to apply these tools using experimental datasets across broad MS applications, including formulations, consumer goods, batteries, pharmaceuticals, and beyond.

Materials Science Webinar

Accelerating OLED innovation with multi-scale, multi-physics simulations

Join us to explore how integrated digital workflows drive the design of next-generation, high-performance OLEDs.

Materials Science Case Study

The Future of Food: Molecular Simulations and AI/ML Reshaping Product Development

Materials Science Webinar

AI/ML meets physics-based simulations: A new era in complex materials design

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices. 

Materials Science Webinar

Electrodes, electrolytes & interfaces: Harnessing molecular simulation and machine learning for rapid advancements in battery materials development

In this webinar, we demonstrate the application of automated solutions for accurate prediction of electrode materials.

Materials Science Webinar

Schrödinger Materials Science Seminar Japan 2024 

《無料Webセミナー》材料開発向けシミュレーション・ソフトウェアおよびマテリアルズ・インフォマティクスの活用事例を紹介。

Materials Science Webinar

Taking experimentation digital: Materials innovation using atomistic simulation and machine learning at-scale

In this webinar, we introduce a modern approach to materials R&D using a digital chemistry platform for in silico analysis, optimization and discovery.

Materials Science Webinar

In silico materials development: Integrating atomistic simulation into academic chemistry and engineering labs

In this webinar, we explore Schrödinger’s leading physics-based and machine learning computational technologies and provide a comprehensive introduction to the capabilities of computational modeling in chemistry, materials science, and engineering.

Materials Science Webinar

Data-driven materials innovation: Where machine learning meets physics

In this webinar, we demonstrate how Schrödinger’s tools can help overcome these common challenges by using a combination of physics-based simulation data, enterprise informatics, and chemistry-informed ML.

Materials Science Webinar

Cutting-Edge Cosmetics: Innovating for Sustainability with Machine Learning & Molecular Simulations

In this webinar, we explore the challenges chemists face, and how new approaches can help find solutions quicker.

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.

Materials Science Documentation

MS Informatics

Automated machine learning tools for materials science applications

Materials Science Documentation

Materials Science Panel Explorer

Quickly learn which Schrödinger tools are the best fit for your research.

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.

View more tutorials

Related Products

Learn more about the related computational technologies available to progress your research projects.

MS Formulation ML

Automated machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties

MS Force Field Applications

Cutting-edge force field technologies for accurate property predictions

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

OLED Device ML

Machine learning solution to investigate relationships between the architecture and performance of OLED devices for accelerated screening

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 Publication

Screening Antioxidant Ingredients Using Quantum Mechanics and Machine Learning

Materials Science Publication

Band Gap and Reorganization Energy Prediction of Conducting Polymers by the Integration of Machine Learning and Density Functional Theory

Materials Science Publication

Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures

Materials Science Publication

Advancing efficiency in deep-blue OLEDs: Exploring a machine learning–driven multiresonance TADF molecular design

Materials Science Publication

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

Materials Science Publication

Machine learning-based design of pincer catalysts for polymerization reaction

Materials Science Publication

Conformers influence on UV-absorbance of avobenzone

Materials Science Publication

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

Materials Science Publication

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

Materials Science Publication

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

View all publications

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.

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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.

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Other Resources