论文标题
通过机器学习增强的原子结构的全球优化
Global optimization of atomistic structure enhanced by machine learning
论文作者
论文摘要
使用第一原理能量表达式（GOFEE）的全球优化是一种有效的方法，用于鉴定计算昂贵的能量景观中低能结构，例如密度功能理论（DFT）所描述的范围，范德华启用DFT，甚至是DFT以外的方法。 Gofee依靠机器学习的能量和力量的替代模型，并在线训练，探索配置空间，消除了使用第一原理方法对所有候选结构进行昂贵放松的需求。在本文中，我们详细阐述了在高斯过程回归（GPR）替代模型中使用具有两个长度尺度的高斯内核的重要性。我们进一步探讨了在较低置信度的GOFEE中的作用，以放松和选择候选结构。此外，我们提供了两种改进的方法：1）现在，人口产生依赖于用DFT评估的所有低能结构的聚类，每个集群中每个群集中最低的能量成员组成了人群。 2）现在最终的剥削步骤中采样精心采样的盆地中最终的放松是在第一原理方法中作为持续的放松路径进行的，以允许对最佳结构进行任意良好的放松，独立于对替代模型的预测分辨率分辨率。通过应用它来识别富勒烯型气相富勒烯型24原子碳液和圆顶形状的18个原子碳簇的低能结构来证明GOFEE方法的多功能性（111）。
Global Optimization with First-principles Energy Expressions (GOFEE) is an efficient method for identifying low energy structures in computationally expensive energy landscapes such as the ones described by density functional theory (DFT), van der Waals-enabled DFT, or even methods beyond DFT. GOFEE relies on a machine learned surrogate model of energies and forces, trained on-the-fly, to explore configuration space, eliminating the need for expensive relaxations of all candidate structures using first-principles methods. In this paper we elaborate on the importance of the use of a Gaussian kernel with two length scales in the Gaussian Process Regression (GPR) surrogate model. We further explore the role of the use in GOFEE of the lower confidence bound for relaxation and selection of candidate structures. In addition, we present two improvements to the method: 1) the population generation now relies on a clustering of all low-energy structures evaluated with DFT, with the lowest energy member of each cluster making up the population. 2) the very final relaxations in well-sampled basins of the energy landscape, the final exploitation steps, are now performed as continued relaxation paths within the first-principles method, to allow for arbitrarily fine relaxations of the best structures, independently of the predictive resolution of the surrogate model. The versatility of the GOFEE method is demonstrated by applying it to identify the low-energy structures of gas-phase fullerene-type 24-atom carbon clusters and of dome-shaped 18-atom carbon clusters supported on Ir(111).
