"Machine learning in chemistry and beyond" (ChE-651) seminar by Simon Batzner "Equivariant Interatomic Potentials"
Representations of atomistic structures for machine learning must transform predictably under the geometric transformations of 3D space, in particular rotation, translation, and reflection as well as permutation of atoms of…
24-05-202224-05-2022With: Simon is a PhD student in Applied Mathematics at Harvard. While interested in far too many things for his own good, his research focuses on building deep learning systems for applications in computational physics and chemistry. Before joining Harvard, he worked on machine learning at MIT and on the NASA mission SOFIA. In his free time, you can find him playing soccer, hiking, and swimming. He comes to Harvard having finished his Master’s at MIT. He is originally from beautiful Illertissen, Germany.
Online: https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUWJyQT09
Category: Conferences – Seminars
