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arXiv 2011.04604cond-mat.mtrl-sci
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生成精确神经网络势的系统方法:碳的情况

A Systematic Approach to Generating Accurate Neural Network Potentials: the Case of Carbon

Yusuf Shaidu, Emine Kucukbenli, Ruggero Lot, Franco Pellegrini, Efthimios Kaxiras, Stefano de Gironcoli

摘要:

可负担得起的和广泛适用的原子间相互作用势的可用性是解锁现代材料建模的财富所需的关键。基于人工神经网络的方法产生的潜力是有前途的,然而,神经网络训练需要大量的数据,充分采样,从一个往往未知的势能面。在这里,我们提出了一个自洽的方法,是基于晶体结构预测形式主义和指导下的无监督数据分析,构建一个准确的,廉价的和可转移的人工神经网络的潜力。使用这种方法,我们构建了一个碳原子间势,并证明了它的能力,以重现第一原理的结果,金刚石,石墨和石墨烯的弹性和振动特性,以及能量排序和结构特性的范围广泛的结晶和非晶相。

英文摘要:

Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modelling. Artificial neural network based approaches for generating potentials are promising; however neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for Carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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