Machine Learning‐Assisted Discovery of 2D Perovskites with Tailored Bandgap for Solar Cells

Abstract 2D organic–inorganic halide perovskites (OIHPs) have received considerable attention due to their attractive photoelectronic properties. Nevertheless, the selection of components for 2D OIHPs with target bandgap is still challenging. To address this issue, a collaborative machine learning model to screen promising 2D OIHPs materials with tailored bandgap is established. Based on the high‐throughput screening via machine learning model, 18 materials with bandgap of 0.9–1.6 eV are obtained to meet the requirement of Shockley–Queisser theory. And considering the application of weak light indoor, 30 candidates with bandgap of about 2.0 eV are also screened out successfully. The prediction results are verified to be reliable by comparing with the published results. Moreover, the Shapley Additive exPlanation and statistical analysis indicate that the electronegativity of the X site, the electronegativity of B site, the vertical ionization potential of A site, and the number of inorganic layers play decisive roles in the prediction of bandgap. This collaborative prediction combining with interpretable strategies can achieve rapid and accurate prediction of bandgap, thus accelerating the development of the 2D OIHP materials in various applications.