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.

中文翻译：

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机器学习辅助发现用于太阳能电池的具有定制带隙的二维钙钛矿

二维有机-无机卤化物钙钛矿（OIHP）由于其有吸引力的光电特性而受到广泛关注。然而，选择具有目标带隙的 2D OIHP 组件仍然具有挑战性。为了解决这个问题，建立了一种协作机器学习模型，用于筛选具有定制带隙的有前景的二维 OIHP 材料。基于机器学习模型的高通量筛选，获得了18种带隙为0.9-1.6 eV的材料，满足Shockley-Queisser理论的要求。考虑到室内弱光应用，也成功筛选出带隙约为2.0 eV的30个候选材料。通过与已发表的结果进行比较，验证了预测结果的可靠性。而且，Shapley加性解释和统计分析表明，X位点的电负性、B位点的电负性、A位点的垂直电离势以及无机层的数量在带隙的预测中起决定性作用。这种协同预测与可解释策略相结合可以实现快速准确的带隙预测，从而加速二维OIHP材料在各种应用中的发展。