Author:
Alsaui Abdulmohsen,Alqahtani Saad M.,Mumtaz Faisal,Ibrahim Alsayoud G.,Mohammed Alghadeer,Muqaibel Ali H.,Rashkeev Sergey N.,Baloch Ahmer A. B.,Alharbi Fahhad H.
Abstract
AbstractOne of the most challenging problems in condensed matter physics is to predict crystal structure just from the chemical formula of the material. In this work, we present a robust machine learning (ML) predictor for the crystal point group of ternary materials (A$$_l$$
l
B$$_m$$
m
C$$_n$$
n
) - as first step to predict the structure - with very small set of ionic and positional fundamental features. From ML perspective, the problem is strenuous due to multi-labelity, multi-class, and data imbalance. The resulted prediction is very reliable as high balanced accuracies are obtained by different ML methods. Many similarity-based approaches resulted in a balanced accuracy above 95% indicating that the physics is well captured by the reduced set of features; namely, stoichiometry, ionic radii, ionization energies, and oxidation states for each of the three elements in the ternary compound. The accuracy is not limited by the approach; but rather by the limited data points and we should expect higher accuracy prediction by having more reliable data.
Publisher
Springer Science and Business Media LLC
Cited by
7 articles.
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