Screener and enumerator with force-field optimization (SEFFO): Algorithm for searching adsorption sites and configurations on 2D materials

With the increasing attention to 2D materials for photocatalytic applications, as well as to data science, there is a need for high-throughput computation of adsorption states for experimentally or theoretically discovered structures in order to study (photo-) catalytic mechanism. Despite numerous progresses in high-throughput methods for adsorption study, a general search algorithm is lacking. In this work, SEFFO (Screener and Enumerator with Force-Field Optimization) algorithm is developed for the automation of adsorption study on 2D material surface. Graph theory is utilized to create the descriptors of the adsorption configurations, which are later input for geometry construction by numerical optimization. The configuration screening process is combining the use of graphs with structural similarity comparison of configurations density functional theory (DFT) produced configurations. The algorithm is validated through four case studies, involving water and carbon dioxide molecules as adsorbates, molybdenum sulfide and carbon nitride as substrate counterparts. The results are consistent with literature while proposing alternative configurations. Additionally, SEFFO can show the evolution between configurations during the process. This method enables the high throughput study of adsorption behavior on 2D materials, and paves the way for future surface studies involving other substrate/adsorbates pairs.