论文标题
Mlatticeabc：使用机器学习对晶体材料的通用晶格恒定预测
MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning
论文作者
论文摘要
晶格常数（例如单位细胞边缘长度和平面角）是晶体材料周期结构的重要参数。预测晶格常数在晶体结构预测和材料性质预测中广泛应用。先前的工作使用了机器学习模型，例如神经网络和支持向量机，结合了晶格恒定预测的组成功能，并获得了平均$ r^2 $ 0.82的立方结构的最大性能。由于结构的同质性，针对固定形式的特殊材料家族（例如ABX3钙钛矿）量身定制的其他型号可以实现更高的性能。但是，这些经过小型数据集训练的模型通常不适用于具有不同组成物质的材料的通用晶格参数预测。在此，我们报告了MlatticeABC，这是一种随机森林机器学习模型，其新的描述符设置用于晶格单位细胞边缘长度（$ a，b，c $）预测，该预测的晶格参数$ a $ a $ a $立方体晶体的R2得分为0.979，并且对其他晶体系统也有重大的性能改进。可以在https://github.com/usccolumbia/mlatticeabc上自由访问源代码和训练有素的模型
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials property prediction. Previous work has used machine learning models such as neural networks and support vector machines combined with composition features for lattice constant prediction and has achieved a maximum performance for cubic structures with an average $R^2$ of 0.82. Other models tailored for special materials family of a fixed form such as ABX3 perovskites can achieve much higher performance due to the homogeneity of the structures. However, these models trained with small datasets are usually not applicable to generic lattice parameter prediction of materials with diverse compositions. Herein, we report MLatticeABC, a random forest machine learning model with a new descriptor set for lattice unit cell edge length ($a,b,c$) prediction which achieves an R2 score of 0.979 for lattice parameter $a$ of cubic crystals and significant performance improvement for other crystal systems as well. Source code and trained models can be freely accessed at https://github.com/usccolumbia/MLatticeABC
