通过机器学习解开结构混乱:等原子三次相的结构预测和多态化 ABC
Anton O Oliynyk1,2, Lawrence A Adutwum2, Brent W Rudyk2
1Department of Chemistry, University of Houston , Houston, Texas 77204, United States.
Journal of the American Chemical Society
|November 14, 2017
概括
这项研究引入了一种机器学习模型,使用元素数据预测三元化合物的晶体结构. 该模型准确地预测结构,并确定结构不确定性的区域,通过实验合成进行验证.
科学领域:
- 材料科学
- 计算材料科学
- 晶体学
背景情况:
- 预测新材料的晶体结构对于材料的发现至关重要.
- 均原子三元化合物表现出多样化的晶体结构.
- 现有的方法通常需要大量的实验数据或复杂的模拟.
研究的目的:
- 开发一种基于构成元素的机器学习模型来预测三元晶体结构.
- 研究元素特性对结构偏好的影响.
- 分析和预测三元化合物的多态性.
主要方法:
- 使用集群分辨率特征选择 (CR-FS) 和支持矢量机器 (SVM) 分类.
- 在1037个三元化合物上训练模型并对519个额外的化合物进行验证.
- 确定了113个有影响力的元素变量 (例如大小,电子负性,价值电子,组号).
主要成果:
- 基于组成的晶体结构实现了高预测准确度 (96.9%).
- 证明该模型具有高可靠性的多态差异化能力 (> 0.7).
- 确定了一个"混"区域 (保证值为0.3-0.7),其中可能存在多重多态.
结论:
- 开发的机器学习方法可靠地预测等原子三元化合物的晶体结构.
- 该模型准确地预测和区分多态,揭示结构不确定性的区域.
- TiFeP的实验合成证实了TiNiSi和ZrNiAl类结构的预测共存.
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