Data-Driven Discovery of Transition Metal Dichalcogenide-Based Z-Scheme Photocatalytic Heterostructures

The Z-scheme heterostructure is a highlypromisingphotocatalyst for its unique electronic structure. However, a thoroughexamination of the heterostructure design space through experimentalor computational means is prohibitively expensive. Here, we proposea highly efficient data-driven approach for fast discovering van derWaals (vdW) Z-scheme heterostructures, bypassingthe need for costly calculations and experimentation. By conductinghigh-throughput calculations with the Heyd-Scuseria-Ernzerhofhybrid density functional (HSE06), we first generate a variety ofdata of electronic structures for 18 experimentally synthesized 2Dtransition metal dichalcogenides (TMDs) and 20 of 153 heterostructures(constructed with the 18 TMDs). Using these data, we develop an innovativeand robust descriptor: Allen material electronegativity.Leveraging this descriptor, we identify 27 2D vdW Z-scheme heterostructures from the pool of 153 heterostructures withoutexpensive HSE calculations. We finally refine our findings by selectingsix Z-scheme heterostructures with minimal latticemismatch, further validating them using high-fidelity ab initio calculations and studying their optical absorption. Our researchnot only paves the way for discovering high-performance Z-scheme photocatalysts using data-driven methods but also contributesa universal charge transfer mechanism for vdW device applications.

Automated summary

This study proposes a highly efficient data-driven approach to discover high-performance vdW Z-scheme heterostructure photocatalysts, bypassing expensive calculations and experimentation. By conducting high-throughput calculations and using an innovative descriptor, 27 2D vdW Z-scheme heterostructures were identified without costly calculations. Six heterostructures with minimal lattice mismatch were selected and validated using high-fidelity calculations and optical absorption studies.