Abstract
Strategies combining high-throughput (HT) and machine learning (ML) to accelerate the discovery of promising new materials have garnered immense attention in recent years. The knowledge of new guiding principles is usually scarce in such studies, essentially due to the ‘black-box’ nature of the ML models. Therefore, we devised an intuitive method of interpreting such opaque ML models through SHapley Additive exPlanations (SHAP) values and coupling them with the HT approach for finding efficient 2D water-splitting photocatalysts. We developed a new database of 3099 2D materials consisting of metals connected to six ligands in an octahedral geometry, termed as 2DO (octahedral 2D materials) database. The ML models were constructed using a combination of composition and chemical hardness-based features to gain insights into the thermodynamic and overall stabilities. Most importantly, it distinguished the target properties of the isocompositional 2DO materials differing in bond connectivities by combining the advantages of both elemental and structural features. The interpretable ML regression, classification, and data analysis lead to a new hypothesis that the highly stable 2DO materials follow the HSAB principle. The most stable 2DO materials were further screened based on suitable band gaps within the visible region and band alignments with respect to standard redox potentials using the GW method, resulting in 21 potential candidates. Moreover, HfSe2 and ZrSe2 were found to have high solar-to-hydrogen efficiencies reaching their theoretical limits. The proposed methodology will enable materials scientists and engineers to formulate predictive models, which will be accurate, physically interpretable, transferable, and computationally tractable.
Highlights
Hydrogen is one of the most promising fuels, which can meet the ever-increasing energy demands
Database generation layered double hydroxides (LDHs) have been widely employed for electrocatalytic reactions such as water oxidation[17] and have been extensively utilized as photoelectrocatalysts[18]
An interpretable ML (iML)-HT approach is developed towards establishing structure-stability relationships for finding efficient 2D photocatalysts
Summary
Hydrogen is one of the most promising fuels, which can meet the ever-increasing energy demands. 2D materials offer several advantages such as increased active sites per surface area, enhanced charge separation and transport over their bulk counterparts[2]. They mitigate the viability of charge carrier recombination by reducing the distance required for photogenerated electrons and holes for reaching the active sites. The other notable 2D materials to exhibit promising photocatalytic properties are the 2D layered double hydroxides (LDHs)[8,9] These 2D materials belong to the octahedral symmetry group (Oh) or 1T phase, in which six hydroxyl ligands (OH−1) are attached to a metal atom in the octahedral geometry. It is primarily due to the fact that both theoretical and experimental investigations are driven by chemical intuitions
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