Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
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DOI：
10.1016/j.commatsci.2021.110686
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发表时间：
2021-07-06
影响因子：
3.3
通讯作者：
Hu, Jianjun
中科院分区：
文献类型：
--
作者：
Li, Yuxin;Dong, Rongzhi;Hu, Jianjun
空间群和晶系等几何信息对晶体材料的性质起着重要的作用。因此，晶系和空间群的预测在晶体材料性质预测和结构预测中具有广泛的应用。以前的工作实验X射线衍射（XRD）和密度泛函理论（DFT）的结构测定方法取得了优异的性能，但它们不适用于大规模筛选的材料组成。也有使用Magpie描述符的机器学习模型用于基于成分的材料空间群确定，但它们的预测精度在不同种类的晶体中仅在0.638和0.907之间。在此，我们报告了一种改进的机器学习模型，用于仅使用分子式信息来预测无机材料的晶系和空间群。从材料项目数据库下载的数据集的基准研究表明，我们的随机森林模型的基础上，我们的新的描述符集，实现显着的性能改善与以前的工作相比，准确度分数范围在0.712和0.961之间的空间群分类。我们的模型也显示了晶体系统预测的大的性能改善。经过训练的模型和源代码可在以下网址免费获得：https：github.com/Yuxinya/SG_predict
Geometric information such as the space groups and crystal systems plays an important role in the properties of crystal materials. Prediction of crystal system and space group thus has wide applications in crystal material property estimation and structure prediction. Previous works on experimental X-ray diffraction (XRD) and density functional theory (DFT) based structure determination methods achieved outstanding performance, but they are not applicable for large-scale screening of materials compositions. There are also machine learning models using Magpie descriptors for composition based material space group determination, but their prediction accuracy only ranges between 0.638 and 0.907 in different kinds of crystals. Herein, we report an improved machine learning model for predicting the crystal system and space group of inorganic materials using only the formula information. Benchmark study on a dataset downloaded from Materials Project Database shows that our random forest models based on our new descriptor set, achieve significant performance improvements compared with previous work with accuracy scores ranging between 0.712 and 0.961 in terms of space group classification. Our model also shows large performance improvement for crystal system prediction. Trained models and source code are freely available athttps://github.com/Yuxinya/SG_predict