Formulation machine learning and optimization for accelerated materials discovery

Finding new chemistries and materials, or reformulating existing chemicals, with good properties necessitate extensive trial-and-error experimentation that is costly and inefficient when faced with expansive possibilities. Schrödinger’s Materials Science Suite is a physics-informed, data-driven platform that offers an alternative, cost-effective solution by helping to guide the next round of experiments with scalable, automated digital tools.

Join our upcoming webinar to learn how your R&D team can leverage automated data driven solutions to guide the design of versatile chemical solutions. In this webinar, we will focus on how materials informatics, formulation machine learning, and optimization tools can be applied to create accurate chemical prediction models, screen large libraries, and provide optimized solutions based on desired target properties. We will demonstrate how easy it is to apply these tools using experimental datasets across broad material science applications, including polymers, consumer goods, batteries, and beyond.

Join us and see demos on:

• Training accurate drug solubility ML models for pure or binary liquids as a function of temperature
• Applying optimization tools to tailor formulation design for desired target properties, which provides rapid suggestions for the next best experiments
• Transferability of these tools to be applicable to diverse chemical domains, such as  polymers, consumer goods, batteries, and more, which demonstrates their broad usefulness for diverse applications

Who should attend:

• R&D leaders
• Innovation managers
• Digitization managers
• Computational materials scientists
• Materials scientists
• Pharmaceutical scientists
• Formulation scientists
• Synthetic chemists