“Equivariance and universal approximation for geometric point clouds”
Friday March 10, 2023 | Time 9:30am CET
As with many fields of science, machine learning has become an essential part of the toolbox for modeling matter at the atomic scale, with many frameworks having become well-established, and many more being developed in new research directions.
The most effective frameworks treat atomic structures as point clouds, and incorporate fundamental physical principles, such as symmetry, locality, and hierarchical decompositions of the interactions between atoms, in the construction of the ML model.
I will present a general framework that unifies several of the most recent developments in the field, including the representation of structures in terms of systematically-convergent atom-centered correlations of the neighbor density, as well as equivariant message-passing schemes that build automatically descriptors with equivalent information content.
Rationalizing the structure of equivariant models reveals some limitations, including the existence of configurations that cannot be distinguished by certain classes of symmetric models, and strategies to address them, building accurate and interpretable models that are capable of universal approximation.
Michele Ceriotti received his Ph.D. in Physics from ETH Zürich. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling, in the institute of Materials at EPFL, that focuses on method development for atomistic materials modeling based on statistical mechanics and machine learning. He is especially proud of his contributions to the development of several open-source software packages, including http://ipi-code.org and http://chemiscope.org, and of serving the atomistic modeling community as an associate editor of the Journal of Chemical Physics, as a moderator of the physics.chem-ph section of the arXiv, and as an editorial board member of Physical Review Materials.