Speaker:

Saientan Bag, Senior Scientist I, Schrödinger

Abstract:

Artificial Intelligence (AI) and machine learning (ML) are reshaping materials science, accelerating the discovery of novel materials and optimizing formulations with unprecedented speed and precision. From polymers to catalysts, these tools unlock design possibilities once thought unattainable. But can AI/ML succeed without a foundation in physics and chemistry? Can we overlook decades of scientific understanding in favor of purely data-driven approaches? At Schrödinger, we combine physics-based simulations with ML built on chemically meaningful representations. This synergy improves accuracy, reduces experimental costs, and delivers insights even in data-limited scenarios. In this webinar, we will explore how Schrödinger’s AI/ML approach is transforming materials R&D through real-world case studies. Our innovation operates on two levels: first, by improving the accuracy-efficiency trade-off in atomistic simulations through the development of machine learning force fields (MLFFs) for high-throughput, accurate modeling; and second, by directly applying AI/ML techniques to predict and optimize material properties in applications such as consumer goods, battery electrolytes, polymers, and catalysts.