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原子化系统的一般化代表性结构

James M Goff¹, Coreen Mullen^1,2, Shizhong Yang³

¹Center for Computing Research, Sandia National Laboratories, Albuquerque, NM 87185, United States of America.

Journal of physics. Condensed matter : an Institute of Physics journal

|November 26, 2024

概括

一种新的通用代表结构 (GRS) 方法产生了捕捉复杂材料特征的小型原子结构. 这种数据驱动的方法有效地代表了各种系统,包括液体和合金,推动了材料的发现.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
数据科学数据科学数据科学

背景情况:

现有的生成代表性原子结构的方法通常仅限于晶体系统和固定的晶格.
在小型结构中的大型合金中复制化学混乱等基本特征是具有挑战性的.
对原子系统进行更一般的描述需要全面的原子环境描述.

研究的目的:

提出一种新的数据驱动方法,用于生成代表任意物质系统,相或合并的小型原子结构.
通过不将结构限制在固定格子上来克服以前方法的局限性.
为了使高化学和空间复杂性具有代表性的原子结构的高效和系统的生成.

主要方法:

使用原子集群扩张 (ACE) 基础来获得一个完整的原子环境描述符集.
实施通用代表结构 (GRS) 方法,以基于ACE描述符分布生成结构.
采用优化算法,有效地生成各种材料表示.

主要成果:

证明了对晶体系统,无形材料和液体产生系统地可改进的表示的能力.
突出显示了具有同等信息内容的原子化机器学习训练数据集的缩小表示.
展示了液态相的小 (40-72原子) 表示和新型结构生成的潜力.

结论:

GRS方法提供了一种强大的,可通用的方法,用于在各种材料类型中创建具有代表性的原子结构.
这种方法比现有的数据驱动和高对称性受限技术具有显著的优势.
GRS促进了高效的材料建模,数据压缩和对新型材料结构的探索.

关键词:

原子集群扩张原子集群扩张原子结构的生成原子结构的生成计算材料计算材料机器学习描述符分子动力学分子动力学

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