Efficient Global Structure Optimization with a Machine-Learned Surrogate Model
Efficient Global Structure Optimization with a Machine-Learned Surrogate Model
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DOI：
10.1103/physrevlett.124.086102
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发表时间：
2020-02-27
影响因子：
8.6
通讯作者：
Hammer, Bjork
中科院分区：
文献类型：
--
作者：
Bisbo, Malthe K.;Hammer, Bjork
提出了一种利用原子结构的第一性原理能量表达式进行全局优化的方案。在展开搜索的同时，该方法主动学习势能景观的代理模型，在该模型上执行一些局部放松(开发)和进一步的构造搜索(探索)。假设高斯过程，部署两个单独的核宽度以更好地捕捉能量格局的粗略特征，同时保持局部最小值的良好分辨率，使用获取函数来决定所得到的结构中哪一个更有希望并且应该在第一原理水平上处理。结果表明，该方法在寻找曲面重建方面比基于第一原理的进化算法高出两个数量级。最后，利用第一原理能量表达式的全局最优化方法识别了石墨烯薄膜在Ir(111)表面的边缘氧化和氧插层的初始阶段。
We propose a scheme for global optimization with first-principles energy expressions of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a number of local relaxations (exploitation) and further structural searches (exploration). Assuming Gaussian processes, deploying two separate kernel widths to better capture rough features of the energy landscape while retaining a good resolution of local minima, an acquisition function is used to decide on which of the resulting structures is the more promising and should be treated at the first-principles level. The method is demonstrated to outperform by 2 orders of magnitude a well established first-principles based evolutionary algorithm in finding surface reconstructions. Finally, global optimization with first-principles energy expressions is utilized to identify initial stages of the edge oxidation and oxygen intercalation of graphene sheets on the Ir(111) surface.