Document Type
Article
Abstract
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 coefficient of determination (R2) 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 data sets 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 0.973 for lattice parameter a of cubic crystals with an average R2 score of 0.80 for a prediction of all crystal systems. The R2 scores are between 0.498 and 0.757 over lattice b and c prediction performance of the model, which could be used by just inputting the molecular formula of the crystal material to get the lattice constants. Our results also show significant performance improvement for lattice angle predictions. Source code and trained models can be freely accessed at https://github.com/usccolumbia/MLatticeABC.
Digital Object Identifier (DOI)
Publication Info
Published in ACS Omega, Volume 6, Issue 17, 2021, pages 11585-11594.
Rights
© 2022 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0.
APA Citation
Li, Y., Yang, W., Dong, R., & Hu, J. (2021). Mlatticeabc: Generic lattice constant prediction of crystal materials using machine learning. ACS Omega, 6(17), 11585–11594. https://doi.org/10.1021/acsomega.1c00781