Stan Moore

Sandia National Laboratories — United States

Materials Science · Physical Sciences

14h-index11.2kcitations100works3.52yr avg
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Research Topics

Machine Learning in Materials Science(12), Vacuum and Plasma Arcs(11), Parallel Computing and Optimization Techniques(9), Distributed and Parallel Computing Systems(7), Spectroscopy and Quantum Chemical Studies(7)

Publications100 total

LAMMPS-KOKKOS: Performance Portable Molecular Dynamics Across Exascale Architectures
2025· 2 citedOpen Access
Breaking the mold: Overcoming the time constraints of molecular dynamics on general-purpose hardware
The Journal of Chemical Physics·2025· 2 citedOpen Access
Toward physically realistic ablation modeling in direct simulation Monte Carlo
Physics of Fluids·2024· 5 cited
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System
2024· 10 cited
Breaking the mold: overcoming the time constraints of molecular dynamics on general-purpose hardware
arXiv (Cornell University)·2024Open Access
LAMMPS and SPARTA: Performance Portability of Particle Methods Through Kokkos
2024Open Access
Recent Enhancements to the SPARTA DSMC Code
2024Open Access
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System
arXiv (Cornell University)·2024Open Access
Extreme Metastability of Diamond and its Transformation to the BC8 Post-Diamond Phase of Carbon
The Journal of Physical Chemistry Letters·2024· 16 citedOpen Access
Extreme Metastability of Diamond and its Transformation to BC8 Post-Diamond Phase of Carbon
arXiv (Cornell University)·2024Open Access
LAMMPS and SPARTA: Performance Portability Through Kokkos
2023Open Access
Frontier: Exploring Exascale
2023· 81 citedOpen Access
SPARTA Users Meeting
2023Open Access
Accelerator Packages and Performance
2023Open Access
Optimizing GPU Performance: Case Study Using the Chain Benchmark in LAMMPS
2023Open Access
GPU Programming in LAMMPS via Kokkos
2023Open Access
LAMMPS: Performance Portability Pre-ECP and Post-ECP
2023Open Access
LAMMPS/SNAP Journey through ECP
2023Open Access
Machine learning interatomic potential for simulations of carbon at extreme conditions
Physical review. B./Physical review. B·2022· 53 cited
Extreme-Scale Atomistic Simulations of Molten Metal Expansion.
2022Open Access
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Frequent Co-authors

Aidan P. Thompson(9), Paul Crozier(6), Steven J. Plimpton(6), Mitchell Wood(6), Danny Pérez(5), Christian Robert Trott(4), Jonathan Willman(4), Kien Nguyen-Cong(4), A. B. Belonoshko(4), Ivan Oleynik(4), Matthew Hopkins(4), Hasan Metin Aktulga(3), Mark J. Stevens(3), Jason Koski(3), Evan Weinberg(3), Dean R. Wheeler(3), Kylee Santos(3), Tomas Oppelstrup(3), A Sharifian(3), Ilya Sharapov(3)