机器学习支持的退火用于预测巨正则晶体结构
Machine learning supported annealing for prediction of grand canonical crystal structures
AI总结：
本文研究将因子分解机与量子退火（FMQA）用于晶体结构预测，以构型能量为损失函数，验证其在快速基态采样和恢复亚稳态局部极小值顺序方面的有效性。
摘要：
本研究探讨了将因子分解机与量子退火（FMQA）相结合应用于材料科学中的晶体结构问题（CSP）。FMQA是一种黑箱优化算法，它结合机器学习与退火机来寻找使给定损失最小化的黑箱函数样本。CSP涉及根据材料的化学组成确定原子在材料中的最优排列方式，这是材料科学中的一个关键挑战。我们通过将损失函数设定为由预定义原子间势给出的晶体构型能量，探索FMQA高效采样最优晶体构型的能力。此外，我们研究了该算法对各种亚稳态构型（即势能的局部极小值）能量的学习效果。我们的研究表明，FMQA在快速基态采样以及恢复局部极小值之间的关联顺序方面具有潜力。
英文摘要：
This study investigates the application of Factorization Machines with Quantum Annealing (FMQA) to address the crystal structure problem (CSP) in materials science. FMQA is a black-box optimization algorithm that combines machine learning with annealing machines to find samples to a black-box function that minimize a given loss. The CSP involves determining the optimal arrangement of atoms in a material based on its chemical composition, a critical challenge in materials science. We explore FMQA's ability to efficiently sample optimal crystal configurations by setting the loss function to the energy of the crystal configuration as given by a predefined interatomic potential. Further we investigate how well the energies of the various metastable configurations, or local minima of the potential, are learned by the algorithm. Our investigation reveals FMQA's potential in quick ground state sampling and in recovering relational order between local minima.
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