Toward ab Initio Ground States of Gold Clusters via Neural Network Modeling
Toward ab Initio Ground States of Gold Clusters via Neural Network Modeling
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DOI：
10.1021/acs.jpcc.9b08517
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Aidan Thorn;J. Rojas-Nunez;S. Hajinazar;S. Baltazar;A. Kolmogorov
中科院分区：
文献类型：
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作者：
Aidan Thorn;J. Rojas-Nunez;S. Hajinazar;S. Baltazar;A. Kolmogorov
预先筛选具有可靠经典势的候选结构是加速从头计算基态搜索的有效途径。鉴于机器学习力场的日益普及，令人惊讶的是，很少有工作致力于量化它们在全局结构优化中相对于传统潜力的优势。在这项研究中，我们开发了一个神经网络（NN）模型，并系统地将其与常用的Gupta势和嵌入原子模型进行基准测试，以寻找稳定的AuN团簇（30≤N≤80）。我们最近推出的多部落进化算法实现了一个有效的同时优化的集群在全尺寸范围内。密度泛函理论（DFT）的候选人的配置与三个经典模型的评估表明，NN结构的能量低至少10毫电子伏/原子的51个尺寸中的30个。我们还表明，DFT评估所有NN松弛结构在进化搜索结果…
Prescreening candidate structures with reliable classical potentials is an effective way to accelerate ab-initio ground state searches. Given the growing popularity of machine learning force fields, surprisingly little work has been dedicated to quantifying their advantages over traditional potentials in global structure optimizations. In this study, we have developed a neural network (NN) model and systematically benchmarked it against a commonly used Gupta potential and an embedded atom model in the search for stable AuN clusters (30≤N≤80). An efficient simultaneous optimization of clusters in the full size range was achieved with our recently introduced multitribe evolutionary algorithm. Density functional theory (DFT) evaluations of candidate configurations identified with the three classical models revealed that the NN structures were lower in energy by at least 10 meV/atom for 30 of the 51 sizes. We also demonstrated that DFT evaluation of all NN-relaxed structures during evolutionary searches resul...