Prediction of Crystal Symmetry Groups for Binary and Ternary Materials from Chemical Compositions using Machine Learning

Data-driven modeling becomes a fundamental and integral approach to conduct scientific work besides experiment, theory, and computation. It is applied in almost all scientific and technological fields. In material sciences, it is used to expedite the discovery of new materials with pre-specified properties. This shall allow the leap toward the next generation’s related sets of materials. In this work, starting only from the chemical formula, the elemental properties are utilized to develop an accurate predictive ML model for the crystallographic symmetry groups classification, including crystal systems, point groups, Bravais lattices and space groups [1,2]. The first step was generating a materials space of all possible ternary and binary compounds based on the common and uncommon oxidation states of 77 elements. The number of possible elemental combinations surpassed 600 million materials in total for both ternary and binary materials [1-3]. The average balanced accuracy of the predictive model exceeded 95% for all symmetry groups. The success of this work will contribute effectively to the advancement of materials science and discovery.

[1] Alsaui, Abdulmohsen, et al. "Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula." Scientific reports 12.1 (2022): 1-10.

[2] Alsaui, Abdulmohsen A., et al. "Resampling techniques for materials informatics: limitations in crystal point groups classification." Journal of Chemical Information and Modeling 62.15 (2022): 3514-3523.

[3] Baloch, Ahmer AB, et al. "Extending Shannon's ionic radii database using machine learning." Physical Review Materials 5.4 (2021): 043804.