MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning

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 R 2 of 0.82. Other models tailored for special materials family of a fixed form such as ABX 3 perovskites can achieve much higher performance due to the homogeneity of the structures. However, these models trained with small datasets are usually not applicable for generic lattice parameter prediction of materials with diverse composition. 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 a R 2 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

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints

“…In many cases, the radius ratio plays an important role. At present, the space group 51,52 and lattice constants 53,54 of crystal structures can be predicted, and the contact map of a given composition can also be predicted by the deep learning method. 24 With the development of machine learning algorithms, we expect that the various inputs required by our multiobjective genetic algorithm for crystal structure reconstruction can be predicted with high precision, so as to allow our CMCrystalMOO to achieve a high-quality crystal structure prediction with only a given composition.…”

Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints

“…In many cases, the radius ratio plays an important role. At present, the space group [51,52] and lattice constants [53,54] of crystal structures can be predicted, and the contact map of a given composition can also be predicted by the deep learning method [24]. With the development of machine learning algorithms, we expect that the various inputs required by our multi-objective genetic algorithm for crystal structure reconstruction can be predicted with high precision, so as to allow our CMCrystalMOO achieves high-quality crystal structure prediction with only a given composition.…”

High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

“…After inspecting the generated structures by the GAN, it was found that the generated lattice parameter a was often not good enough, leading to overlapping atom clusters. To address this issue, an additional post-processing step introduced to predict the lattice length a using a composition based machine learning model that the authors recently developed, [43] which achieved a R 2 score of 0.979 for cubic lattice a prediction.…”