Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Q15: Physical SymmetryAware Machine Learning of Interatomic Interactions and Properties of MaterialsInvited Live Streamed

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Sponsoring Units: GDS DCOMP Chair: Boris Kozinsky, Harvard University Gabor Csanyi Room: McCormick Place W183C 
Wednesday, March 16, 2022 3:00PM  3:36PM 
Q15.00001: Atomic cluster expansion as a platform for constructing atomic scale models Invited Speaker: Ralf Drautz Classical and machine learning interatomic potentials alike incorporate design choices that reflect the intuition of their authors and that are justified only aposterioi by the performance of the model. Design choices comprise, for example, the form of the embedding function of an embedded atom potential or a specific angular dependence of a descriptor in a machine learning potential. 
Wednesday, March 16, 2022 3:36PM  4:12PM 
Q15.00002: TBA Invited Speaker: Alexander Shapeev

Wednesday, March 16, 2022 4:12PM  4:48PM 
Q15.00003: Equivariant Interatomic Potentials Invited Speaker: Simon L Batzner Symmetry plays a central role in the representation of materials for the purpose of Machine Learning. In particular, all sensible representations must obey the symmetries of 3D space: translation, rotation, and inversion, in addition to permutation symmetry with respect to the labeling of atoms. Traditionally, representations have been constructed to possess invariance with respect to the above transformations. In this talk, I will discuss recent efforts to generalize invariance to the broader class of equivariant representations and demonstrate how this leads to a large increase in generalization accuracy and sampleefficiency of the learned models. The talk will then discuss the recently introduced Neural Equivariant Interatomic Potential (NequIP), an E(3)equivariant Interatomic Potential that exhibits unprecedented accuracy and sample efficiency and outperforms invariant potentials with up to 1000x fewer reference data. I will discuss applications of NequIP to a diverse set of materials systems, including Li diffusion, amorphous structures, heterogeneous catalysis, and water. Finally, I will discuss our current theoretical understanding of the role of equivariance and explore connections with existing approaches, such as the atomic cluster expansion. 
Wednesday, March 16, 2022 4:48PM  5:24PM 
Q15.00004: Atomic Cluster Expansion Force Fields for Molecules Invited Speaker: David P Kovacs Machine Learning based force fields have revolutionised the modelling of materials at the atomistic scale. In this talk I will describe Atomic Cluster Expansion (ACE) which provides a systematic framework to derive a formally complete set of symmetric polynomial basis functions that can be used to build highly accurate and fast force fields. I will demonstrate that ACE force fields parametrised using regularised linear regression can compete in accuracy with most Gaussian Process and Neural Network based approaches. In particular, I will describe several applications of ACE to molecular systems where it shows excellent smooth and physical extrapolation to unseen parts of the Potential Energy Surface. Finally, I will demonstrate how the ACE framework can be extended to provide a unifying framework for machine learning potentials which includes message passing neural networks like SchNet and NequIP, BehlerParinallo neural networks as well as the Gaussian Process regression based SOAPGAP approaches. 
Wednesday, March 16, 2022 5:24PM  6:00PM 
Q15.00005: Designing molecular models by machine learning and experimental data Invited Speaker: Cecilia Clementi The last years have seen an immense increase in highthroughput and highresolution technologies for experimental observation as well as highperformance techniques to 
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