EXTENDED ABSTRACT: Two-dimensional (2D) van der Waals (vdW) heterostructures have attracted significant attention for their promising applications in electronics, optoelectronics, and multiferroic devices. These structures offer various modulation possibilities by stacking different 2D materials together. In the first part of my presentation, I will discuss our recent advancements in discovering high-performance vdW heterostructure photocatalysts. Through a combination of big-data analysis, high-throughput screening, high-fidelity calculations, and machine learning, we examined 155 2D semiconductors with diverse structures from our 2DMatPedia database. Using the materials genome descriptor, we identified 1,062 potential Z-scheme vdW heterostructures, with the top 33 candidates confirmed through high-fidelity hybrid functional calculations.The second part will cover our work on utilizing high-throughput calculations and machine learning to identify strong out-ofplane ferroelectricity in vdW sliding ferroelectric materials, both in homo- and hetero-structures. These methods demonstrate the power of high-throughput calculations, materials genome approaches, and machine learning in accelerating the discovery of high-performance vdW materials for a wide range of applications.
Keywords: Materials Genome; High-throughput Calculations; Machine Learning; 2D Materials Database; Heterostructures Photocatalysts; Sliding Ferroelectrics