Copper-based sulfide photocathodes have shown impressive performance in solar water splitting applications due to their narrow bandgaps, high absorption coefficients, and good carrier transport properties. Several factors, such as composition, thickness, and doping, have a direct influence on the onset potential, photocurrent density, and solar-to-hydrogen efficiency. Screening for the optimal combination in the presence of multiple variables is undoubtedly a challenging task. However, constructing a comprehensive database, developing photocathode models, and utilizing machine learning to derive the best results clearly save a significant amount of experimental effort. This approach efficiently reduces the experimental workload, streamlines the process, and expedites the development of high-performance materials for photoelectrochemical water splitting applications. Here, we introduce a comprehensive machine learning process to guide the preparation of copper-based sulfide photocathodes. The random forest model was selected to train and capture the complex relationship between different layers of copper-based sulfide photocathodes and electrolytes to predict unstudied conditions, and the accuracy of the test set reached 96.7%. Through SHAP interpretability analysis, we provide heuristic rules to deepen the understanding of the influence of different factors on the performance of the catalytic system. We also developed a prediction platform to share our prediction models.

中文翻译：

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高性能铜基硫化物光电阴极面的机器学习辅助设计


铜基硫化物光阴极面由于其窄带隙、高吸收系数和良好的载流子传输特性，在太阳能水分解应用中表现出令人印象深刻的性能。成分、厚度和掺杂等几个因素对起始电位、光电流密度和太阳能制氢效率有直接影响。在存在多个变量的情况下筛选最佳组合无疑是一项具有挑战性的任务。然而，构建一个全面的数据库、开发光阴极面模型并利用机器学习来获得最佳结果显然可以节省大量的实验工作。这种方法有效地减少了实验工作量，简化了过程，并加快了用于光电化学分解水应用的高性能材料的开发。在这里，我们介绍了一个全面的机器学习过程来指导铜基硫化物光阴极面的制备。选择随机森林模型来训练和捕获铜基硫化物光阴极面和电解质不同层之间的复杂关系，以预测未研究的条件，测试集的准确率达到 96.7%。通过 SHAP 可解释性分析，我们提供启发式规则，以加深对不同因素对催化系统性能影响的理解。我们还开发了一个预测平台来分享我们的预测模型。