Data-Driven Materials Innovation: Where Machine Learning Meets Physics
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the intersection of machine learning and physics in materials innovation through this comprehensive 48-minute webinar. Discover how Schrödinger's tools address challenges in data-driven AI-based design for materials science and chemistry. Learn about using molecular dynamics simulations to enhance ML model accuracy for viscosity predictions, building interpretable models for Li-ion battery electrolyte ionic conductivity, and improving ML performance for organic electronics properties using density functional theory. Gain insights into AutoQSAR, DeepAutoQSAR, chemical featurization, optoelectronics properties prediction, volatility machine learning, and neural network potentials. Understand the applications of these techniques in diverse fields such as redox flow batteries, transparent conducting oxides, and liquid electrolytes. Explore Schrödinger's LiveDesign® informatics platform and its suitability for various materials and data types.