Data-driven material models for atomistic simulation

Wood, Mitchell A.; Cusentino, Mary A.; Wirth, Brian D.; Thompson, Aidan P.

doi:10.1103/PhysRevB.99.184305

Title: Data-driven material models for atomistic simulation

Journal Article · Thu May 30 00:00:00 EDT 2019 · Physical Review B

DOI:https://doi.org/10.1103/PhysRevB.99.184305· OSTI ID:1529135

Wood, Mitchell A. ^[1]; Cusentino, Mary A. ^[1]; Wirth, Brian D. ^[2]; Thompson, Aidan P. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of Tennessee, Knoxville, TN (United States)

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parametrizations for potentials using traditional approaches. Machine learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Beginning with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time and length scales where the two methods can be directly compared. Here, we use the spectral neighbor analysis potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the approach, we have developed a tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow ( < 2 nm ) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Fusion Energy Sciences (FES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)

Grant/Contract Number:: AC04-94AL85000; NA0003525; 17-SC-20-SC

OSTI ID:: 1529135

Alternate ID(s):: OSTI ID: 1523553

Report Number(s):: SAND-2019-0610J; PRBMDO; 671690

Journal Information:: Physical Review B, Vol. 99, Issue 18; ISSN 2469-9950

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 31 works

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