Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions
Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions
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DOI：
10.1021/acsomega.9b04012
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发表时间：
2020-02
期刊：
影响因子：
4.1
通讯作者：
Yong Zhao;Yuxin Cui;Zheng Xiong;Jing Jin;Zhonghao Liu;Rongzhi Dong;Jianjun Hu
中科院分区：
文献类型：
--
作者：
Yong Zhao;Yuxin Cui;Zheng Xiong;Jing Jin;Zhonghao Liu;Rongzhi Dong;Jianjun Hu
材料的结构信息，如晶体系统和空间群，对于分析其物理性质非常有用。然而，材料的巨大组成空间使得实验X射线衍射（XRD）或基于第一原理的结构确定方法对于组成空间中的大规模材料筛选是不可行的。在这里，我们提出并评估机器学习算法，用于确定材料的结构类型，只考虑它们的成分。我们将随机森林（RF）和多层感知器（MLP）神经网络模型与三种类型的特征相结合：Magpie，原子向量和独热编码（原子频率）用于材料的晶体系统和空间群预测。四种类型的模型预测晶体系统和空间群的建议，训练和评估，包括一对所有的二元分类，多类分类，多态性预测，和多标签分类。合成少数过采样技术（SMOTE）用于减轻不平衡数据集的影响。我们的研究结果表明，RF与喜鹊功能一般优于其他算法的二元和多类预测的晶体系统和空间群，而MLP与原子频率的功能是最好的一个结构多态性预测。对于多标记预测，MLP与原子频率和二进制相关性与喜鹊模型是最好的预测晶体系统和空间群，分别。我们的相关描述符的分析确定了一些关键的结构类型的预测，如电负性，共价半径和门捷列夫数的贡献功能。因此，我们的工作铺平了道路，通过预测材料的结构特性，无机材料的快速组成为基础的结构筛选。
Structural information of materials such as the crystal systems and space groups are highly useful for analyzing their physical properties. However, the enormous composition space of materials makes experimental X-ray diffraction (XRD) or first-principle-based structure determination methods infeasible for large-scale material screening in the composition space. Herein, we propose and evaluate machine-learning algorithms for determining the structure type of materials, given only their compositions. We couple random forest (RF) and multiple layer perceptron (MLP) neural network models with three types of features: Magpie, atom vector, and one-hot encoding (atom frequency) for the crystal system and space group prediction of materials. Four types of models for predicting crystal systems and space groups are proposed, trained, and evaluated including one-versus-all binary classifiers, multiclass classifiers, polymorphism predictors, and multilabel classifiers. The synthetic minority over-sampling technique (SMOTE) is conducted to mitigate the effects of imbalanced data sets. Our results demonstrate that RF with Magpie features generally outperforms other algorithms for binary and multiclass prediction of crystal systems and space groups, while MLP with atom frequency features is the best one for structural polymorphism prediction. For multilabel prediction, MLP with atom frequency and binary relevance with Magpie models are the best for predicting crystal systems and space groups, respectively. Our analysis of the related descriptors identifies a few key contributing features for structural-type prediction such as electronegativity, covalent radius, and Mendeleev number. Our work thus paves a way for fast composition-based structural screening of inorganic materials via predicted material structural properties.