Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures

Abstract

Machine learning (ML) techniques have made enormous progress in the field of materials science. However, many conventional ML algorithms operate as “black-boxes”, lacking transparency in revealing explicit relationships between material features and target properties. To address this, the development of interpretable ML models is essential to drive further advancements in AI-driven materials discovery. In this study, we present an interpretable framework that combines traditional machine learning with symbolic regression, using Janus III–VI vdW heterostructures as a case study. This approach enables fast and accurate predictions of stability and electronic structure. Our results demonstrate that the prediction accuracy using the classification model for stability, based on formation energy, reaches 0.960. On the other hand, the R², MAE, and RMSE value using the regression model for electronic structure prediction, based on band gap, achieves 0.927, 0.113, and 0.141 on the testing set, respectively. Additionally, we identify a universal interpretable descriptor comprising five simple parameters that reveals the underlying physical relationships between the candidate heterostructures and their band gaps. This descriptor not only delivers high accuracy in band gap prediction but also provides explicit physical insight into the material properties.