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Trends_in_Chem_Forum_Euclidean_Symmetry_and_Equivariance_in_ML_20200909.pdf (7.05 MB)
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Euclidean Symmetry and Equivariance in Machine Learning

preprint
submitted on 10.09.2020 and posted on 11.09.2020 by Tess Smidt
Understanding symmetry’s role in the physical sciences is critical for choosing an appropriate machine learning method. While invariant models are the most prevalent symmetry-aware models, equivariant models can more faithfully represent physical interactions. Until recently, equivariant models had been absent in the literature due to their technical complexity. Now, after two years of active development, fully-equivariant Euclidean neural net- works are ready to take on challenges across the physical sciences.

Funding

Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231

History

Email Address of Submitting Author

tsmidt@lbl.gov

Institution

Lawrence Berkeley National Laboratory

Country

United States

ORCID For Submitting Author

0000-0001-5581-5344

Declaration of Conflict of Interest

Authors declare no conflict of interest.

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