Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid‐liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non‐precious transition‐metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human‐interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

中文翻译：

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用于实用锑酸盐电催化剂性能的可解释机器学习模型

由于催化表面及其原位演化的复杂性、不同的反应路径以及电化学中固液界面的存在，计算预测催化剂在反应条件下的性能是一项具有挑战性的任务。我们在这里演示如何找到相对简单的机器学习模型来预测实验观察到的起始电位。我们模型的输入包括非贵重过渡金属氧化锑纳米颗粒催化剂上氧还原反应的数据，结合实验条件和来自密度泛函理论模拟的计算上可承受的块体原子和电子结构描述符。从人类可解释的遗传编程模型中，我们确定了控制这些氧化物起始电位趋势的关键实验描述符和关键补充大量电子和原子结构描述符，并推断出应如何调整这些描述符以增加起始电位。我们最终通过实验确认钪作为镍锑氧化物中的掺杂剂会导致所需的起始电位增加，从而验证了这些机器学习预测。人们发现宏观实验因素是催化性能模型中需要考虑的至关重要的描述符，这凸显了机器学习即使在存在小数据集的情况下也可以发挥重要作用。