Seminar: Numerical Methods for Partial Differential Equations

Wednesday, May 10, 2023 at 4:15pm to 5:15pm

MIT-Math Dept., Room 2-449

Speaker:  Tess Smidt  (MIT)

Title:   Euclidean Symmetry Equivariant Machine Learning for Atomic Systems --

            Overview, Applicatioins, and Open Questions

 

Abstract:

Atomic systems (molecules, crystals, proteins, etc.) are naturally represented by a set of coordinates in 3D space labeled by atom type. This is a challenging representation to use for machine learning because the coordinates are sensitive to 3D rotations, translations, and inversions (the symmetries of 3D Euclidean space). In this talk I’ll give an overview of Euclidean invariance and equivariance in machine learning for atomic systems. Then, I’ll share some recent applications of these methods on a variety of atomistic modeling tasks (ab initio molecular dynamics, prediction of crystal properties, and scaling of electron density predictions). Finally, I’ll explore open questions in expressivity, data-efficiency, and trainability of methods leveraging invariance and equivariance.

 

 

Event Type

Conferences/Seminars/Lectures

Events By Interest

Academic

Events By Audience

Public, MIT Community

Events By School

School of Science

Website

https://math.mmit.edu/nmpde/

Department
Department of Mathematics
Contact Email

daisymae@math.mit.edu

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