Contributed Talk

Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials

Mitchell Wood
Sandia National Laboratories
Ivan Oleynik
Sandia National Laboratories
Mary Alice Cusentino
Sandia National Laboratories
Aidan Thompson Aidan Thompson
Sandia National Laboratories
  • Wednesday, 11 Aug 2021
  • 13:45 - 14:00 EDT
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With exascale super computers arriving in the near future, it is timely to ask whether our simulation software is capable of matching this unprecedented computing capability. While many research challenges in material physics, chemistry and biology lie just out of reach on peta-scale machines due to length and time restrictions inherent to Molecular Dynamics(MD), questions of the accuracy of our simulations will continue to linger. This is particularly true for complex alloys, composites of disparate components as well as materials in extremes of temperature, pressure and radiation exposure. This talk will overview advances made in machine learned Spectral Neighborhood Analysis Potential(SNAP) for both their physical accuracy and computational performance on leadership platforms. Exemplar problems include plasma facing materials, phase transitions of carbon and metals near their triple-point. Additionally, a discussion will be presented of best practices for assembling training data and model form selection for SNAP and related ML interatomic potentials.