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一种适用于极端条件下氪的精确机器学习势。

An Accurate Machine-Learned Potential for Krypton under Extreme Conditions.

作者信息

Iwasaki Asuka J, Kirsz Marcin, Pruteanu Ciprian G, Ackland Graeme J

机构信息

SUPA, School of Physics and Astronomy and Centre for Science at Extreme Conditions, The University of Edinburgh, Edinburgh EH9 3FD, United Kingdom.

出版信息

J Phys Chem Lett. 2025 Feb 13;16(6):1559-1566. doi: 10.1021/acs.jpclett.4c03272. Epub 2025 Feb 4.

Abstract

We have developed two machine-learned pair potentials for krypton based on CCSD(T) quantum chemical calculations on two and three atom clusters. Through extensive testing with molecular dynamics, we find both potentials give good agreement with the experimental equation of state, melting point, and neutron scattering data for the fluid. Compared with the most widely used Lennard-Jones model, our potentials produced similar results in low-pressure melting and equation of state. However, extending the regime to higher pressures of ≤30 GPa showed a remarkable divergence of the Lennard-Jones model from the experimental (solid) equation of state. Our potential showed extremely good agreement, despite having no solid phases in the training set.

摘要

我们基于对两原子和三原子团簇的CCSD(T)量子化学计算,开发了两种氪的机器学习对势。通过分子动力学的广泛测试,我们发现这两种势与流体的实验状态方程、熔点和中子散射数据都有很好的一致性。与使用最广泛的 Lennard-Jones 模型相比,我们的势在低压熔化和状态方程方面产生了相似的结果。然而,将范围扩展到≤30 GPa的更高压力时,Lennard-Jones 模型与实验(固体)状态方程出现了显著差异。尽管训练集中没有固相,但我们的势显示出极佳的一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6053/11831645/eed82c6da5b7/jz4c03272_0001.jpg

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