Bridging Text Mining and Quantum Simulations for the Design of 2D Monochalcogenide Materials

The formulation of a structured framework dedicated to the systematic review of the literature, identification of potential compounds, theoretical quantum chemistry characterization, and assessment of the significance of stability descriptors is essential for accelerating the discovery of two-dimensional materials for technological applications. In this work, we selected the two-dimensional monochalcogenides (MQ), which have attracted increased interest due to their potential applications in future devices, as prototype materials for the present investigation. Our framework started with a natural language processing analysis of more than 5400 articles, revealing a growing research interest in the two-dimensional MQ compounds, especially in electronics, energy, and fundamental studies. This led to the selection of 27 diverse MQ compounds across 13 distinct two-dimensional structural phases for density functional theory calculations, resulting in an extensive database of physicochemical properties. We evaluated formation enthalpies, uncovering clear stability trends (e.g., stability declines with heavier chalcogens, dynamic robustness of GeSe), and evaluated equilibrium lattice parameters, noting predictable expansions and significant anisotropies. Bader charge analysis offered insights into charge-transfer and ionicity trends. Our electronic structure analysis identified band gaps (direct or indirect, depending on the M element group and the weight of the chalcogen) and optical absorption anisotropies significantly influenced by crystal symmetry and spin orbit coupling. Importantly, band alignment calculations classified all possible heterojunctions as type I, II, or III, underscoring their extensive potential for future optoelectronic device development. Additionally, we incorporated machine learning, using a random forest approach, along with our density functional theory calculations to accurately forecast trends in energetic properties. This analysis identified the electronic charge as a highly significant stability descriptor among the 34 descriptors, underscoring its importance for future machine learning endeavors. Hence, this study offers a streamlined framework for the characterization and discovery of promising two-dimensional materials, highlighting the synergy between data-driven analysis and quantum-based simulations.