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Pietro Guccione¹, Domenico Diacono², Stefano Toso³

¹Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, via Orabona 4, Bari 70125, Italy.

|September 5, 2023

概括

本研究介绍了一种新的机器学习方法,用于从原子对分布函数 (PDF) 配置文件中确定晶体细胞参数. 该方法成功估计了纳米和准无形材料的晶格特性,克服了初始结构解决方案的先前限制.

科学领域:

材料科学材料科学材料科学
晶体学晶体学是指结晶学.
数据科学数据科学数据科学

背景情况:

原子对分布函数 (PDF) 方法对纳米材料进行了先进的结构分析,使得短距离的顺序调查成为可能.
然而,使用PDF的初始晶体结构确定是具有挑战性的,因为难以识别单元细胞特性.

研究的目的:

开发和验证一种新的两步方法,用于直接从PDF档案中估计晶体细胞参数.
提高确定纳米和准无形材料晶体学性质的准确性和可行性.

主要方法:

使用机器学习方法,特别是k-最近邻居,从PDF档案中推断晶体细胞类型.
使用多变量分析和矢量叠加技术来提取精确的细胞参数.
使用反复性定量分析,从PDF档案中导出用于机器学习输入的描述符.

主要成果:

对整个PDF档案应用的k-最近邻居分类器在格子确定中显示出最高的性能.
细胞参数提取步骤根据细胞度量表现出不同的精度:单度细胞为40%,二度细胞为20%,三度细胞为5%.
用真实纳米晶体获得了有希望的结果,即使有显著的晶体杂质,也能识别正确或近乎正确的单元细胞.

结论:

提出的方法为从PDF数据中确定晶体学性质提供了显著的进步,特别是对于具有挑战性的纳米和准无形材料.
这种方法提高了初始晶体结构解决方案的能力,扩大了PDF分析在材料科学中的应用.

关键词:

晶体细胞参数的参数在水晶格子上.机器学习是机器学习.多变量分析多变量分析.纳米晶体是一种纳米晶体.配对分布函数的配对分布函数矢量叠加的位置矢量叠加.

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