Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
School of Materials Science and Engineering, Jiangsu University, Zhenjiang, Jiangsu, China.
Nat Commun. 2023 Feb 11;14(1):792. doi: 10.1038/s41467-023-36322-5.
The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, PtRu-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, PtIr, and PtRu. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.
电化学氨氧化为氮气作为能源和环境应用的一种手段，是实现可持续氮循环的关键技术。包括 Pt 及其与 Ir 的双金属在内的最先进的金属催化剂具有很有前景的活性，尽管它们在可观的电流密度下具有很高的过电势，而且贵金属价格飞涨。在此，通过基于从头算数据训练的图神经网络来探索三元 Pt 合金纳米结构的巨大设计空间，以同时预测位点反应性、表面稳定性和催化剂可合成性描述符。在主动学习工作流程中出现的少数无 Ir 候选物中，PtRu-M（M：Fe、Co 或 Ni）合金被成功合成并通过实验验证，其氨氧化活性高于 Pt、PtIr 和 PtRu。更重要的是，使用基于机器学习的位点基元表示进行的特征归因分析为金属表面的化学键提供了基本的见解，并为超越结合位点的 d 带中心度量的高性能催化体系的设计策略提供了思路。
