Generalization of SNAP to arbitrary machine-learning interatomic potentials in LAMMPS
ORAL
Abstract
SNAP is an automated methodology for generating accurate and robust application-specific machine-learning interatomic potentials (MLIAPs) in LAMMPS. The MLIAP package generalizes SNAP to arbitrary MLIAPs. This is accomplished by separating the energy model (e.g. linear, non-linear, Gaussian process, neural network) from the local atomic neighborhood descriptors (e.g. ACE, Behler-Parrinello, DeepPot, SNAP, SOAP). Any new model added to the MLIAP package can be combined with any existing descriptor to compute energy and forces, and vice versa. Gradients of energy and forces w.r.t. model parameters can also be computed for training MLIAPs against ab initio data. I will discuss the underlying algorithms and describe some interesting applications.
*Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy
or the United States Government.
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Presenters
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Aidan Thompson
- Sandia National Laboratories