Explore machine learning techniques for atomic-scale modeling in this comprehensive lecture by Michele Ceriotti from École Polytechnique Fédérale de Lausanne (EPFL). Delve into the world of interatomic potentials based on machine learning and their impact on extending the length and time scales accessible to explicit atomistic simulations. Discover solutions to challenges in the field, including poor scaling with chemical diversity and the incorporation of functional properties beyond interatomic potentials. Learn about a scheme that compresses chemical information to reduce model costs, and its application in constructing a potential for 25 d-block transition metals. Examine the convergence of quantum and data-driven approaches through examples of simulations involving electronic charge density, single-particle Hamiltonian, and electron density of states for matter with thermally-excited electrons.
Machine Learning for Atomic-Scale Modeling - Potentials and Beyond - IPAM at UCLA
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA
Taught by
Institute for Pure & Applied Mathematics (IPAM)
Related Courses
-
Machine Learning for 3D Optical Imaging - IPAM at UCLA
-
Uncertainty-Aware Machine Learning Models of Many-Body Atomic Interactions
-
Machine Learning in Condensed Matter and Materials Physics
-
From Electrons to the Simulation of Materials - IPAM at UCLA
-
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
-
Learning ML Interatomic Potentials - Day 2
