Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Z43: Large-Scale First Principles Atomistic Simulation: Recent Advances and New ChallengesInvited Live Streamed
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Sponsoring Units: DCOMP GDS GSCCM Chair: Ivan Oleynik, University of South Florida; Aidan Thompson, Sandia National Laboratories Room: McCormick Place W-375B |
Friday, March 18, 2022 11:30AM - 12:06PM |
Z43.00001: Automated parameterization of the atomic cluster expansion for predicting phase stability and mechanical properties Invited Speaker: Ralf Drautz The atomic cluster expansion (ACE) provides a general and mathematically complete representation of the properties of interacting atoms [1-3]. ACE has been implemented in the LAMMPS molecular dynamics simulation software package and its numerical efficiency is competitive or superior to other ML potentials [4]. In this presentation I will focus on the parameterization of ACE from first principles reference data and the computation of thermodynamic and mechanical properties. |
Friday, March 18, 2022 12:06PM - 12:42PM |
Z43.00002: Symmetry Considerations for Machine Learning Algorithms Operating on 3D Geometry and Physical Data Invited Speaker: Tess E Smidt Symmetry is a pervading and multi-facetted concept in physics: The symmetry of space gives rise to mathematical objects that transform predictably under a change in coordinate system (geometry and geometric tensors). The symmetry of physical systems determines which properties are allowed or require symmetry breaking mechanisms to occur (Curie's Principle). The differentiable symmetries of physics give rise to conservation laws (Noether's theorem). In this talk, I will discuss methods for merging the rigors of these many flavors of symmetry with the flexibility of machine learning methods. As a demonstrative example, I'll focus on the properties of Euclidean Neural Networks which are constructed to preserve 3D Euclidean symmetry. These principles can be extended to other spaces and their relevant symmetries. Perhaps unsurprisingly, symmetry preserving machine learning algorithms are extremely data-efficient; they are able to achieve better results with less training data. More unexpectedly, they also act as "symmetry-compilers": they can only learn tasks that are symmetrically well-posed and in the case of differentiable methods they can also help uncover when there is symmetry implied missing information. I'll give examples of these properties and how they can be used to craft useful training tasks for physical data. To conclude, I'll highlight some open questions in merging symmetry and machine learning techniques particularly relevant to representing physical systems. |
Friday, March 18, 2022 12:42PM - 1:18PM |
Z43.00003: Large Scale Simulations with the Deep Potential Method Invited Speaker: Roberto Car
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Friday, March 18, 2022 1:18PM - 1:54PM |
Z43.00004: ChIMES: Toward a Machine-Learned Solution for Simulations of Condensed Phase Chemistry Under Extreme Conditions Invited Speaker: Rebecca K Lindsey
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Friday, March 18, 2022 1:54PM - 2:30PM |
Z43.00005: Machine Learning for Molecular Properties: Going Beyond Interatomic Potentials Invited Speaker: Sergei Tretiak Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. Generally, ML provides a surrogate model trained on the dataset of some reference data. This model establishes a relationship between structure and underlying chemical properties, guiding chemical discovery. Designing high-quality training data sets is crucial to overall model accuracy. To address this this problem, I will describe the active learning strategy, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. The locality approximation underpinning favorable computational scaling of the ML models, is another severe limitation that fails to capture long-range effects that may arise from charge transfer, polarization, electrostatic or dispersion interactions. I will also discuss how ML models can overcome nonlocality (via introduction of interaction layers, self-consistent cycles, or charge equilibration schemes) and exemplify their performance for chemical problems with nonlocalities. All these advances are exemplified by applications to molecules and materials. Exciting new method development and explosive growth of user-friendly ML frameworks, designed for chemistry, demonstrate that the field is evolving towards physics-based models augmented by data science. |
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