Michele Ceriotti: Atomic-scale modeling in the age of machine learning

When modeling materials and molecules at the atomic scale, achieving a realistic level of complexity and making quantitative predictions are usually conflicting goals.
Data-driven techniques have made great strides towards enabling simulations of materials in realistic conditions with uncompromising accuracy.
In this talk I will summarize the core concepts that have driven the extraordinarily fast progress of the field, discussing the relationship to more general concepts in geometric machine learning.
I will describe some of the most promising modeling techniques that combine physics-inspired and data-driven paradigms, indicate the most pressing open challenges, and present several compelling examples ranging from water to semiconductors and from metals to molecular materials.