最大值和最小值
贝叶斯优化
插值(计算机图形学)
全局优化
吸附
高斯分布
材料科学
航程(航空)
能量最小化
计算机科学
能量(信号处理)
统计物理学
化学物理
算法
物理
机器学习
人工智能
化学
物理化学
数学
量子力学
运动(物理)
数学分析
复合材料
作者
Sami Kaappa,Casper Larsen,Karsten W. Jacobsen
标识
DOI:10.1103/physrevlett.127.166001
摘要
We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine-learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75.
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