Machine learning prediction of defect formation energies

The feasibility and the stability of a defect, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. Here, using ML techniques we identify the factors that influence defect formation energy in two material classes namely perovskites and MXenes. Using elemental properties as features and random forest regression, we demonstrate a systematic approach to down select the important features, establishing a framework for accurate predictions of the defect formation energy. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection. The framework itself is general and can be applied to other material classes.