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.

中文翻译：

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用于光催化水分解的二维材料的机器学习辅助计算筛选

探索具有特殊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，二维材料的发现目前严重依赖于零散的研究，微调化合物的化学成分和电子特征的机会有限。从作为 2D 材料资源的 V2DB 数字库开始，我们建立并执行了一种漏斗方法，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者。初始筛选步骤基于机器学习 (ML) 预测的属性，后续步骤涉及复杂性不断增加的第一性原理建模，从密度泛函理论 (DFT) 到混合 DFT 再到 GW 计算。确保在每个阶段更复杂的计算仅适用于最有希望的候选者，我们的研究引入了一种有效的筛选方法，该方法可以作为在大型化学空间内加速二维材料发现的模型。我们的筛选过程筛选出 11 种有前途的 2D 光催化剂。