Wang Yatong, Sorkun Murat Cihan, Brocks Geert, Er Süleyman
DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, Eindhoven 5612 AJ, The Netherlands.
Materials Simulation and Modeling, Department of Applied Physics, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands.
J Phys Chem Lett. 2024 May 9;15(18):4983-4991. doi: 10.1021/acs.jpclett.4c00425. Epub 2024 May 1.
The exploration of two-dimensional (2D) materials with exceptional physical and chemical properties is essential for the advancement of solar water splitting technologies. However, the discovery of 2D materials is currently heavily reliant on fragmented studies with limited opportunities for fine-tuning the chemical composition and electronic features of compounds. Starting from the V2DB digital library as a resource of 2D materials, we set up and execute a funnel approach that incorporates multiple screening steps to uncover potential candidates for photocatalytic water splitting. The initial screening step is based upon machine learning (ML) predicted properties, and subsequent steps involve first-principles modeling of increasing complexity, going from density functional theory (DFT) to hybrid-DFT to GW calculations. Ensuring that at each stage more complex calculations are only applied to the most promising candidates, our study introduces an effective screening methodology that may serve as a model for accelerating 2D materials discovery within a large chemical space. Our screening process yields a selection of 11 promising 2D photocatalysts.
探索具有卓越物理和化学性质的二维（2D）材料对于太阳能水分解技术的进步至关重要。然而，目前二维材料的发现严重依赖于零散的研究，对化合物的化学成分和电子特性进行微调的机会有限。我们以V2DB数字图书馆作为二维材料的资源，建立并执行了一种漏斗式方法，该方法包含多个筛选步骤，以发现光催化水分解的潜在候选材料。初始筛选步骤基于机器学习（ML）预测的性质，后续步骤涉及从密度泛函理论（DFT）到杂化DFT再到GW计算的复杂度不断增加的第一性原理建模。我们的研究确保在每个阶段仅将更复杂的计算应用于最有前景的候选材料，从而引入了一种有效的筛选方法，该方法可作为在大型化学空间中加速二维材料发现的模型。我们的筛选过程产生了11种有前景的二维光催化剂。
