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Data-driven material models for atomistic simulation

Physical Review B

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

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. Starting 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. In this work, 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 method, 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 (<2nm) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.

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TYPE Journal Article YEAR 2019
DOIOSTIScopus

Creating stable productive CSE software development and integration processes in unstable environments on the path to exascale

Proceedings - 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science, SE4Science 2019

Bartlett, Roscoe B.; Frye, Joseph R.

The Sandia National Laboratories (SNL) Advanced Technology Development and Mitigation (ATDM) project focuses on R&D for exascale computational science and engineering (CSE) software. Exascale application (APP) codes are co-developed and integrated with a large number of 2^nd generation Trilinos packages built on top of Kokkos for achieving portable performance. These efforts are challenged by needing to develop and test on many unstable and constantly changing pre-exascale platforms using immature compilers and other system software. Challenges, experiences, and lessons learned are presented for creating stable development and integration workflows for these types of difficult projects. In particular, we describe automated workflows, testing, and integration processes as well as new tools and multi-team collaboration processes for effectively keeping a large number of automated builds and tests working on these unstable platforms.

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TYPE Conference Poster YEAR 2019
DOIOSTIScopus
Results 2201–2250 of 9,998
Results 2201–2250 of 9,998