The simulation of materials properties using physics-based approaches, such as density functional theory (DFT) and molecular dynamics (MD), has long been successful in providing insights into structure-property relationships and subsequently aiding in the design of novel materials. In recent years, AI/machine learning (ML) has been used extensively in conjunction with physics-based modeling techniques to greatly accelerate materials innovation. The accuracy and generalizability of physics-based modeling improves the performance of AI/ML models and enables them to be used effectively even in small-data regimes. Conversely, the speed and flexibility of AI/ML help bridge the time- and spatial- scale limitations of physics-based models, creating a synergistic approach that optimizes both predictive accuracy and computational efficiency.

In this webinar, we will 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.

Key Learning Objectives:

• Understand how DFT descriptors enhance the accuracy of AI/ML models for optoelectronic molecules and battery electrolytes
• Discover how MD simulation descriptors improve AI/ML models for the viscosity of organic molecules
• Explore the use of Schrödinger’s automated Formulation Machine Learning solution to:
  • Train AI/ML models for the solubility of APIs in binary solvents
  • Predict the motor octane number of hydrocarbons
• Learn about advances in AI/ML force field technology (QRNN) and its application in modeling the bulk properties of inorganic cathode coating materials