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Machine-learning model shows diamond melting at high pressure


This multi-billion atom simulation of shockwave propagation into initially uncompressed diamond (blue) makes use of a high-accuracy SNAP mannequin from Sandia Nationwide 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: Sandia Nationwide Laboratories

A Sandia Nationwide Laboratories supercomputer simulation mannequin known as SNAP that quickly predicts the habits 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 Evaluation Potential) simulations to crack, soften into amorphous carbon after which recrystallize. The work may support understanding of the inner construction of carbon-based exoplanets and have vital implications for nuclear fusion efforts that make use of capsules made from polycrystalline diamond.

Designing novel supplies and implications for big 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 vital for devising inside fashions of big planets. Highly effective DOE amenities like Sandia’s Z machine and Lawrence Livermore Nationwide Laboratory’s Nationwide Ignition Facility can recreate near-identical situations of those worlds in earthly experiments that provide close-up examinations of radically compressed supplies. However even these uniquely highly effective machines can’t pinpoint key microscopic mechanisms of change below these excessive situations, resulting from limitations in diagnostics on the degree of atoms.

“Solely computer simulations can try this,” 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 Affiliation of Computing Equipment. The diamond-specific modeling, which took solely a day on the Summit supercomputer (the quickest within the U.S.) at Oak Ridge Nationwide Laboratory, was led by Prof. Ivan Oleynik on the University of South Florida. Along with Sandia and USF, the collaborative staff additionally included software developers on the Division of Vitality’s Nationwide Vitality Research Scientific Computing Heart and the NVIDIA Company.

The staff’s simulations relied on SNAP, one of many main machine-learning descriptions of interatomic interactions, to model and clear up a vital drawback, 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 strategies to coach a surrogate mannequin that faithfully reproduced the proper atomic forces. These had been calculated utilizing high-accuracy quantum mechanical calculations, that are solely potential for methods containing a couple of hundred atoms. The surrogate mannequin was then scaled as much as predict forces and accelerations for methods containing billions of atoms. All native atomic constructions that emerged within the large-scale simulations had been well-represented within the small-scale coaching knowledge, a vital situation for accuracy.

One other vital a part of the ultimate outcome was efficiency optimization of the software program to run effectively on GPU-based supercomputers like Summit, stated 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.”

Run time shortened by 97 %

“Since supercomputer time is expensive and highly competitive,” stated Thompson, “each shortening of SNAP’s run time saves money and increases the usefulness of the model.”

Sandia researchers Stan Moore and Mitchell Wooden made vital contributions to the SNAP mannequin and the dramatic efficiency enhancements.

The optimized software program for working SNAP on supercomputers is offered within the open supply distribution of Sandia’s LAMMPS molecular dynamics code. The Sandia FitSNAP software program for constructing new SNAP fashions can be publicly accessible.


Machine-learning technique could improve fusion energy outputs


Extra info:
Kien Nguyen-Cong et al, Billion atom molecular dynamics simulations of carbon at excessive situations and experimental time and size scales, Proceedings of the Worldwide Convention for Excessive Efficiency Computing, Networking, Storage and Evaluation (2021). DOI: 10.1145/3458817.3487400

Quotation:
Machine-learning mannequin reveals diamond melting at excessive strain (2022, January 26)
retrieved 26 January 2022
from https://techxplore.com/information/2022-01-machine-learning-diamond-high-pressure.html

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