Interpretability In Atomic-Scale Machine Learning

Channel:

Simons Institute for the Theory of Computing

Subscribers:

68,700

Published on June 30, 2022 5:34:40 AM ● Video Link: https://www.youtube.com/watch?v=x0ssE6nh73g

Duration: 30:46

389 views

4

Michele Ceriotti (Swiss Federal Institute of Technology in Lausanne) [REMOTE]
https://simons.berkeley.edu/talks/interpretability-atomic-scale-machine-learning-0
Interpretable Machine Learning in Natural and Social Sciences

I will provide a brief overview of some of the established frameworks used to apply machine-learning techniques to the atomistic modeling of matter, and in particular to the construction of surrogate models for quantum mechanical calculations. I will focus in particular on the construction of physics-aware descriptors of the atomic structure - based on symmetrized correlations of the atom density - and how they facilitate the interpretation of regression and classification models based on them.