Toward machine learning for microscopic mechanisms: A formula search for crystal structure stability based on atomic properties

Machine-learning techniques are revolutionizing the way to perform efficient materials modeling. We here propose a combinatorial machine-learning approach to obtain physical formulas based on simple and easily accessible ingredients, such as atomic properties. The latter are used to build materials features that are finally employed, through linear regression, to predict the energetic stability of semiconducting binary compounds with respect to zinc blende and rocksalt crystal structures. The adopted models are trained using a dataset built from first-principles calculations. Our results show that already one-dimensional (1D) formulas well describe the energetics; a simple grid-search optimization of the automatically obtained 1D-formulas enhances the prediction performance at a very small computational cost. In addition, our approach allows one to highlight the role of the different atomic properties involved in the formulas. The computed formulas clearly indicate that “spatial” atomic properties (i.e., radii indicating maximum probability densities for $s,p,d$ electronic shells) drive the stabilization of one crystal structure with respect to the other, suggesting the major relevance of the radius associated with the $p$-shell of the cation species.