GAASP：基于遗传算法的高熵材料原子采样协议
GAASP: Genetic Algorithm Based Atomistic Sampling Protocol for High-Entropy Materials
摘要：
高熵材料是由相对简单的晶格上的多个元素组成的。由于这种材料的多组分性质，原子尺度采样由于组合复杂性而在计算上是昂贵的。我们提出了一种基于遗传算法的方法，这样复杂的化学无序材料的采样。基于遗传算法的原子采样协议（GAASP）变体可以生成低能量和高能量结构。GAASP低能量变体与大都市准则结合，避免了过早收敛，并确保了细致平衡条件。GAASP可用于生成用于热力学预测的低能结构，以及可用于机器学习应用的各种结构。
英文摘要：
High-Entropy Materials are composed of multiple elements on comparatively simpler lattices. Due to the multicomponent nature of such materials, the atomic scale sampling is computationally expensive due to the combinatorial complexity. We propose a genetic algorithm based methodology for sampling such complex chemically-disordered materials. Genetic Algorithm based Atomistic Sampling Protocol (GAASP) variants can generate low and well as high-energy structures. GAASP low-energy variant in conjugation with metropolis criteria avoids the premature convergence as well as ensures the detailed balance condition. GAASP can be employed to generate the low-energy structures for thermodynamic predictions as well as diverse structures can be generated for machine learning applications.
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