Formulation ML
Automated machine learning solution to generate accurate formulation-property relationships and screen new formulations with desired properties
Create accurate machine learning models to design better formulations
Formulation ML allows scientists to predict properties based on ingredient structures and compositions. Whether you are a formulation expert or just learning in this area, this automated, supervised learning solution enables you to gain deeper insight into formulation-property relationships.
Key Capabilities
Build formulation-property models for chemical mixtures with varying ingredient structures and compositions, which are scalable up to 100 ingredients or more
Rapidly predict novel formulations with new chemistry and composition, requiring only seconds per formulation
Understand which molecular features to focus on to fine-tune properties, leveraging feature importance tools to identify key descriptors for a property using a trained model
Enable accurate ML model development using expert cheminformatic descriptors and automatic hyperparameter tuning with minimal ML expertise
Input customized descriptors, including experimental data, in CSV format into the ML model to improve model performance
Optimize multiple properties simultaneously by modulating ingredient structure and compositions with trained ML models, providing suggestions of best formulations for the next experiment
Featured Resources
White Paper
Materials Science
Webinar
Materials Science
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.
Webinar
Life Science
Materials Science
Accelerating pharmaceutical formulations using machine learning approaches
In this webinar, we will demonstrate how Schrödinger’s integrated ML- and physics-based approaches are transforming pharmaceutical formulation design.
Broad applications across materials science research areas
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Publications
Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures, Alex, C., et al. npj Comput Mater 11, 72, 2025, https://doi.org/10.1038/s41524-025-01552-2.
Software & services to meet your organizational needs
Software Platform
Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.
Research Services
Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.
Support & Training
Access expert support, educational materials, and training resources designed for both novice and experienced users.