Chalcogen Impurity Barriers in 2D Systems via Semi-Empirical/Machine Learning Modeling: A Survey of 4000 Materials

Adequate characterization of two-dimensional (2D) materials with low energy barriers for impurity adsorption is key for advancing applications based on catalysis, sensing, and surface functionalization. However, first-principles methods, such as density functional theory, are often computationally extremely expensive for feasible large-scale screenings. Given such a scenario, we address a data-driven approach, which integrates the semiempirical extended Hückel method (EHM) with machine learning (ML) techniques to estimate adsorption energy barriers in the case of three relevant chalcogen impurities, sulfur (S), selenium (Se), and tellurium (Te). With this aim, we consider the 4036 2D materials found in the Computational 2D Materials Database (C2DB). The scheme employs the EHM to compute energy profiles along three in-plane migration paths, from which average barriers can be derived. The equilibrium distance between the impurity and the 2D surface is not calculated from a time-consuming geometry optimization. Instead, it is estimated from a simple effective phenomenological expression. Physicochemical descriptors are then obtained from the Matminer (Materials Data Mining) library for curated features. Four different ML models are tested, with XGBoost (considering hyperparameter optimization via Optuna) leading to the highest performance. We further use SHAP to verify the resulting predictions, focusing on the ∼1500 materials displaying the lowest barrier values. As could be anticipated, we establish that the average valence electron count, electronegativity, and atomic number are typically the most relevant attributes to validate the ML model. However, we are also able to determine, for the different chalcogen atoms, which other few descriptors likewise considerably influence the adsorption properties. Our results show that when combined with interpretable ML protocols, EHM (and potentially semiempirical calculations in general) can produce a scalable framework for choosing 2D structures that exhibit the desired capture/release dynamics pertinent in a variety of utilization.