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Atomic Structure Optimization with Machine-Learning Enabled Interpolation between Chemical Elements
Physical Review Letters ( IF 8.6 ) Pub Date : 2021-10-14 , DOI: 10.1103/physrevlett.127.166001
Sami Kaappa 1 , Casper Larsen 1 , Karsten Wedel Jacobsen 1
Affiliation  

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.

中文翻译:

通过机器学习实现化学元素间插值的原子结构优化

我们介绍了一种用于原子系统中结构和排序的全局优化的计算方法。该方法依赖于化学元素之间的插值,该插值包含在机器学习结构指纹中。该方法基于高斯过程的贝叶斯优化,适用于 Au-Cu 本体系统、具有 CO 吸附的 Cu-Ni 表面和 Cu-Ni 簇的全局优化。该方法始终如一地识别低能量结构,它们可能是能量的全局最小值。对于具有 23-66 个原子的研究系统,所需的能量和力计算数量在 3-75 范围内。
更新日期:2021-10-14
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