Large ML Potentials for Chemistry - Generalization, Inductive Biases, and Error Cancellation

Explore the cutting-edge developments in machine learning potentials for chemistry in this hour-long lecture by Zack Ulissi from Meta. Delve into the crucial aspects of generalization, inductive biases, and error cancellation in large ML models applied to chemical systems. Gain insights into how these advanced techniques are strengthening the connection between artificial intelligence and scientific research, particularly in the field of chemistry. Learn about the latest methodologies and approaches that are revolutionizing the way we understand and predict chemical behaviors using AI-driven models.

Large ML potentials for chemistry: generalization, inductive biases, and cancellation of errors