Prediction of 2D Materials for Energy Applications Using Computational Methods

The recent application of data-driven methods for molecule and material discovery has shown significant promise. In this context, we have developed an artificial intelligence (AI)-aided virtual screening recipe for two-dimensional (2D) materials discovery, enhancing the search for new materials with specific physical and chemical properties. As part of this effort, we have established the Virtual 2D Materials Database (V2DB), a publicly available resource that includes potentially stable 2D materials along with their AI-predicted key physicochemical properties [1].<br/><br/>Our subsequent focus is on pinpointing the functional 2D materials constituted by abundant chemical elements for energy applications, particularly those suitable for solar-driven photocatalytic water splitting to produce hydrogen (H2). The challenge lies in efficiently navigating the vast chemical space to identify promising 2D materials. To tackle this, we utilize a data-centric approach, screening the V2DB to find stable candidates with appropriate band gaps and optimal photocatalytic properties. This robust virtual screening process incorporates machine learning (ML), high-throughput density functional theory (HT-DFT), hybrid-DFT, and GW calculations.<br/><br/>Through this approach, we have identified 27 new 2D materials that show good potential for photocatalytic water splitting [2,3]. We then performed a detailed analysis of their solar water splitting properties, including electronic and optical features, solar-to-hydrogen conversion efficiency, and carrier mobility. These studies not only introduce new 2D photocatalysts but also highlight the efficiency of a data-driven strategy in systematically exploring materials in an extensive chemical space.<br/><br/>Our approach is versatile in identifying materials with specific properties for various renewable energy applications, including photovoltaic systems and electro/photo-catalytic conversion of feedstock molecules like H₂O, CO₂, and N₂ into valuable fuels and products, thereby exploring previously uncharted chemical spaces of 2D materials.<br/><br/><b>References:</b><br/>[1] M.C. Sorkun, S. Astruc, J.M.V.A. Koelman, S. Er, <i>npj Computational Materials</i> 6, 106 (2020).<br/>[2] Y. Wang, M.C. Sorkun, G. Brocks, S. Er, <i>The Journal of Physical Chemistry Letters</i> 15, 4983 (2024).<br/>[3] Y. Wang, G. Brocks, S. Er, <i>ACS Catalysis</i> 14, 1336 (2024).