This multi-billion atom simulation of shockwave propagation into initially uncompressed diamond (blue) makes use of a high-accuracy SNAP mannequin from Sandia National Laboratories to foretell that the ultimate state (orange) is shaped by recrystallization of amorphous cracks (purple) that take form within the gentle blue, inexperienced and yellow compressed materials. Credit: Image with colours added, courtesy Sandia National Laboratories
Hardware and software program enhancements shorten ‘run time’ from yr to a day.
A Sandia National Laboratories supercomputer simulation mannequin known as SNAP that quickly predicts the conduct of billions of interacting atoms has captured the melting of diamond when compressed by excessive pressures and temperatures. At a number of million atmospheres, the inflexible carbon lattice of the toughest identified substance on Earth is proven in SNAP (Spectral Neighbor Analysis Potential) simulations to crack, soften into amorphous carbon after which recrystallize. The work might assist understanding of the interior construction of carbon-based exoplanets and have necessary implications for nuclear fusion efforts that make use of capsules product of polycrystalline diamond.
Designing novel supplies and implications for large planets
“We can now study the response of many materials under the same extreme pressures,” stated Sandia scientist Aidan Thompson, who originated SNAP. “Applications include planetary science questions — for example, what kind of impact stress would have led to the formation of our moon. It also opens the door to design and manufacture of novel materials at extreme conditions.”
The impact of maximum pressures and temperatures on supplies is also necessary for devising inside fashions of large planets. Powerful DOE services like Sandia’s Z machine and Lawrence Livermore National Laboratory’s National Ignition Facility can recreate near-identical circumstances of those worlds in earthly experiments that provide close-up examinations of radically compressed supplies. But even these uniquely highly effective machines can’t pinpoint key microscopic mechanisms of change beneath these excessive circumstances, resulting from limitations in diagnostics on the degree of atoms.
“Only computer simulations can do that,” stated Thompson.
Gordon Bell paper finalist is about ‘a micron-sized hunk of compressed diamond’
A technical paper describing the simulation was chosen as a finalist for the Gordon Bell prize, sponsored yearly by the Association of Computing Machinery. The diamond-specific modeling, which took solely a day on the Summit supercomputer (the quickest within the U.S.) at Oak Ridge National Laboratory, was led by Prof. Ivan Oleynik on the University of South Florida. In addition to Sandia and USF, the collaborative staff additionally included software program builders on the Department of Energy’s National Energy Research Scientific Computing Center and the NVIDIA Corporation.
The staff’s simulations relied on SNAP, one of many main machine-learning descriptions of interatomic interactions, to mannequin and clear up a vital downside, stated Thompson.
“We created gigantic simulations of a micron-sized hunk of compressed diamond,” stated Thompson. “To do this, we track the motion of billions of atoms by repeatedly calculating the atomic forces over very many, exceedingly tiny, intervals of time.”
Machine-learning bridged with quantum mechanical calculations
SNAP used machine-learning and different knowledge science methods to coach a surrogate mannequin that faithfully reproduced the right atomic forces. These have been calculated utilizing high-accuracy quantum mechanical calculations, which are only possible for systems containing a few hundred atoms. The surrogate model was then scaled up to predict forces and accelerations for systems containing billions of atoms. All local atomic structures that emerged in the large-scale simulations were well-represented in the small-scale training data, a necessary condition for accuracy.
Another critical part of the final result was performance optimization of the software to run efficiently on GPU-based supercomputers like Summit, said Thompson. “Since 2018, just by improving the software, we have been able to make the SNAP code over 30 times faster, shortening the time for these kinds of simulations by 97%. At the same time, each generation of hardware is more powerful than the last. As a result, calculations that might have until recently taken an entire year can now be run in a day on Summit.”
The graph demonstrates the dramatic improvement in computational speed achieved by Sandia National Laboratories’ SNAP model from 2018 to 2021. Credit: Sandia National Laboratories
Run time shortened by 97 percent
“Since supercomputer time is expensive and highly competitive,” said Thompson, “each shortening of SNAP’s run time saves money and increases the usefulness of the model.”
Sandia researchers Stan Moore and Mitchell Wood made important contributions to the SNAP model and the dramatic performance improvements.
The optimized software for running SNAP on supercomputers is available in the open source distribution of Sandia’s LAMMPS molecular dynamics code. The Sandia FitSNAP software for building new SNAP models is also publicly available.
The first version of SNAP was created in 2012 with support from Sandia’s Laboratory Directed Research and Development program. Software improvement has been supported continuously since 2017 by the DOE Exascale Computing Project, a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.
