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

TL;DR

This study develops a scalable, data-driven framework combining semi-empirical methods and machine learning to predict impurity adsorption barriers in 2D materials, enabling large-scale screening for catalysis and sensing applications.

Contribution

It introduces a novel approach integrating Extended Huckel Method with ML models, especially XGBoost, for efficient barrier prediction across thousands of 2D materials.

Findings

XGBoost achieved the best prediction performance.

Valence electron count, electronegativity, and atomic number are key descriptors.

The framework effectively identifies 2D materials with low impurity barriers.

Abstract

Adequate characterization of two-dimensional 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 DFT, are often computationally extremely expensive for feasible large-scale screenings. Given such a scenario, we address a data-driven approach which integrates the semi-empirical Extended Huckel Method with machine learning techniques to estimate adsorption energy barriers in the case of three relevant chalcogen impurities, S, Se and Te. With this aim, we consider the 4036 2D materials found in the 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 tie-consuming geometry optimization. Instead, it is estimated from a simple effective phenomenological expression. Physicochemical descriptors are then obtained from the Matminer library for curated features. Four different ML models are tested, with the XGBoost leading to the highest performance. We further use SHAP to verify the resulting predictions, focusing on the $\sim1,500$ materials displaying the lowest barrier values. As it 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. But we also are 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 can produce a scalable framework for choosing 2D structures that exhibit the desired capture/release dynamics pertinent in a variety of utilization.