CRANN and AMBER Research Centres, School of Chemistry, Trinity College Dublin, College Green, Dublin 2, Ireland.
Molecules. 2021 Oct 21;26(21):6362. doi: 10.3390/molecules26216362.
The oxygen evolution reaction (OER) can enable green hydrogen production; however, the state-of-the-art catalysts for this reaction are composed of prohibitively expensive materials. In addition, cheap catalysts have associated overpotentials that render the reaction inefficient. This impels the search to discover novel catalysts for this reaction computationally. In this communication, we present machine learning algorithms to enhance the hypothetical screening of molecular OER catalysts. By predicting calculated binding energies using Gaussian process regression (GPR) models and applying active learning schemes, we provide evidence that our algorithm can improve computational efficiency by guiding simulations towards candidates with promising OER descriptor values. Furthermore, we derive an acquisition function that, when maximized, can identify catalysts that can exhibit theoretical overpotentials that circumvent the constraints imposed by linear scaling relations by attempting to enforce a specific mechanism. Finally, we provide a brief perspective on the appropriate sets of molecules to consider when screening complexes that could be stable and active for this reaction.
氧气析出反应 (OER) 可用于绿色制氢; 然而，该反应的最先进催化剂由极其昂贵的材料组成。此外，廉价的催化剂具有与之相关的过电势，从而使反应效率降低。这促使人们在计算上寻找该反应的新型催化剂。在本通讯中，我们提出了机器学习算法来增强分子 OER 催化剂的假设筛选。通过使用高斯过程回归 (GPR) 模型预测计算结合能，并应用主动学习方案，我们证明我们的算法可以通过将模拟引导至具有有前途的 OER 描述符值的候选物来提高计算效率。此外，我们推导出一个获取函数，当最大化时，可以识别出可以表现出理论过电势的催化剂，这些过电势可以通过尝试强制特定机制来规避线性标度关系施加的限制。最后，我们简要介绍了在筛选可能对该反应稳定和有效的配合物时应考虑的适当分子集。
