Structural prediction of carbon cluster isomers with machine-learning potential

Duy Huy Nguyen
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Abstract

Structural prediction of low-energy isomers of carbon twelve-atom clusters is carried out using the recently developed machine-learning potential GAP-20. The GAP-20 agrees with density-functional theory calculations regarding geometric structures and average C-C bond lengths for most isomers. However, the GAP-20 substantially lowers the energies of cage-like structures, resulting in a wrong ground state. A comparison of the cohesive energies with the density-functional theory points out that the GAP-20 only gives good results for monocyclic rings. Two multicyclic rings appear as new low-energy isomers, which have yet to be discovered in previous research.
利用机器学习潜力预测碳簇异构体的结构
利用最近开发的机器学习势能 GAP-20 对碳十二原子团簇的低能异构体进行了结构预测。就大多数异构体的几何结构和平均 C-C 键长度而言,GAP-20 与密度泛函理论计算结果一致。然而,GAP-20 大大降低了笼状结构的能量,导致基态错误。将内聚能与密度泛函理论进行比较后发现,GAP-20 只为单环提供了良好的结果。有两个多环作为新的低能异构体出现,这在以前的研究中尚未发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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