发表机构
Jeju National University; Korea Hydro and Nuclear Power Co. Ltd.（济州大学; 韩国水力核电公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出物理残差机器学习方法，利用Tafel方程外推与学习残差修正，从稀疏极化测量预测超出训练范围的析氧催化剂活性，显著降低误差并缩短测量时间。
AI 中文摘要
在组合库上筛选析氧催化剂时，需要决定哪些候选材料应接受剩余的测量。决定性的活性位于每个候选材料测量的电位窗口之外，且通常高于拟合期间记录的所有活性值。我们通过物理残差机器学习来预测该活性：Tafel方程外推候选材料自身的测量电流和斜率，一个随特征空间距离衰减的学习残差修正幅度，以及一个适用域评分在测量前识别超出训练范围的预测。在一个单独制备的含322个候选材料的库中，282个候选材料高于训练最大值，每个候选材料两次测量得到的平均绝对误差为0.203 mA cm$^{-2}$，而所选数据驱动机器学习模型的误差为1.330。训练范围内的误差保持相当，且35个标记催化剂足以拟合该模型。在两个独立数据集中，相同的构建将过电位误差降低了29%至52%。因此，实验活动可以缩短每次测量时间，同时仍能对活性最高的组成进行排序。
英文摘要
Discovery campaigns for oxygen evolution reaction catalysts repeatedly choose, make and measure catalysts. High-throughput platforms stop polarization curves below potentials that damage the catalyst, so the endpoint, the activity at a target potential or current density, often lies beyond the measured window, and the catalysts of most interest are more active than any measured before. Existing methods do not predict these endpoints accurately when few or no endpoints of a new library have been measured. Here we present physics-residual machine learning (PR-ML), which predicts each endpoint as the sum of a Tafel term, computed from the catalyst's own measured curve with an estimated slope, and a residual term learned from labelled catalysts. In twelve Ni-Pd-Pt-Ru thin-film libraries, the current density at 1.70 V$_{RHE}$ was predicted from the currents at 1.40 and 1.55 V$_{RHE}$. Fitted only on earlier libraries, with ridge regression as the residual learner, PR-ML predicted the Ni--Ru library, whose currents mostly exceed theirs, with a mean absolute error of 0.194 mA cm$^{-2}$, against 1.330-1.882 for data-driven models. With five endpoints from the new library and extremely randomized trees as the residual learner, PR-ML gave a similar error, which the same learner used alone reached only with 20, and identified 63-83% of the catalysts more active than the best labelled catalyst, against 2%. In two independent datasets, this fraction rose from at most 1% to 33-95%. Our approach supplies catalyst selection with accurate endpoints beyond the measured part of each curve and above all earlier measurements.
