Prediction of material stability of two-dimensional semiconductors: An interpretable machine learning perspective

Abstract

Despite significant advancements in leveraging artificial intelligence (AI) for drug design, materials science, and other fields, the question of how each dataset feature influences a target metric—essential for constructing better predictive models and targeted materials design—remains largely unaddressed. In this study, we explored the application of interpretable machine learning (ML) techniques to the inverse design of two-dimensional (2D) semiconductor materials, a critical yet underexplored area within the AI4Science domain. Our approach utilized a dataset from the C2DB database, incorporating advanced feature engineering and data imputation strategies to predict material stability, a key determinant of a materials industrial and academic value. Through the calculation of Shapley additive explanation scores and counterfactual analysis, we provided a nuanced understanding of feature contributions toward material stability, enabling the targeted design of 2D semiconductors with optimized properties. This work not only fills the gap in the current literature by emphasizing the role of interpretability in materials design but also demonstrates the potential of interpretable ML in guiding the development of novel materials with enhanced performance characteristics.