Machine learning driven prediction of lattice constants in transition metal dichalcogenides

Abstract

Machine learning represents an emerging branch of artificial intelligence, centering on the enhancement of algorithms in computer programs through the utilization of data and the accumulation of research-driven knowledge. The requirement for artificial intelligence in materials science is essential due to the significant need for innovative high-performance materials on a large scale. In this report, the gradient boosting regression tree model of machine learning was applied to predict the lattice constants of cubic and trigonal MX2 systems (M=transition metal and X=chalcogen atoms). The theoretical/experimental values of the materials were compared to the predicted values to calculate the standard errors such as RMSE (root mean square error) and MAE (mean absolute error). The features used to predict lattice constants were ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density and oxidation states. The features versus contribution barplot has been drawn to reveal the contribution level of each parameter in the degree of [0,1] to obtain the predictions. This report provides a precise account of the prediction methodology for lattice parameters of the transition metal dichalcogenides family, a process that was previously not reported.