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.
M. L. Pereira, M. G. E. da Luz, P. Cesana et al.· Journal of Chemical Theory a...· 0 citations
Superatoms are clusters with physical or chemical properties that bear certain similarities to atoms. We calculated equilibrium geometries and properties using spin-polarized density functional theory (DFT) within the generalized gradient approximation (PBE). We also performed Ab Initio Molecular Dynamics (AIMD) simulations in the NVT ensemble with a Nosé-Hoover thermostat. In addition, Monte Carlo simulations with a potential fitted to DFT were carried out to assess a number of clusters as possible superatoms. The clusters we studied have a chemical composition AB2C10, where A, B, and C are metals, and a structure derived from that of a 13-atom icosahedron. We adopt an operational definition of superatom to assess clusters. According to this definition, superatoms should have (i) a quasispherical equilibrium geometry and (ii) a set of valence orbitals delocalized over the cluster; (iii) they should maintain these two attributes at elevated temperatures (mechanical and thermal stability); and (iv) they should be thermodynamically favored relative to similar clusters. By these criteria, Sn3Y10, CrSn2Zr10, and possibly CrSn2Ti10 can be classified as “superatoms”. We show that cluster stability arises from (i) the intrinsic stability of the C 10 fragment; (ii) its interaction energy with the three “dopant atoms”; and (iii) compatible atomic sizes of the three elements. Clusters such as CrSn2Ti10 and CrSn2Zr10 exhibit an optimal balance among these contributions. They have a high effective coordination, an absence of very low vibrational frequencies, a favorable ratio of their elements’ cohesive energy, and exceptional thermal resilience, with melting temperatures that are well above the weighted average of their elements’ bulk melting points. Electronic analysis shows that CrSn2Ti10 and CrSn2Zr10 display discrete density-of-states features and partial electron delocalization, consistent with superatomic behavior. These electronic properties vanish as the clusters approach the melting point and undergo structural distortion. We show the importance of looking at several, often interrelated, properties in assessing possible superatoms.
Anirudh Krishnadas, João Marcos Tomaz Palheta, Jonathan Bekele Mekonnen et al.· Journal of Physical Chemistr...· 0 citations
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