Das Arunendu, Roy Diptendu, Das Amitabha, Pathak Biswarup
Department of Chemistry, Indian Institute of Technology Indore, Indore 453552, India.
ACS Appl Mater Interfaces. 2024 Oct 30;16(43):58648-58656. doi: 10.1021/acsami.4c12184. Epub 2024 Oct 16.
The electrochemical nitrogen reduction reaction (eNRR) under ambient conditions is a promising method to generate ammonia (NH), a crucial precursor for fertilizers and chemicals, without carbon emissions. Single-atom alloy catalysts (SAACs) have reinvigorated catalytic processes due to their high activity, selectivity, and efficient use of active atoms. Here, we employed density functional theory (DFT) calculations integrated with machine learning (ML) to investigate dodecahedral nanocluster-based SAACs for analyzing structure-activity relationships in eNRR. Over 300 nanocluster-based SAACs were screened with all the transition metals as dopants to develop an ML model predicting stability and catalytic performance. Facet sites were identified as optimal doping positions, particularly with late group transition metals showing superior stability and activity. Utilizing DFT+ML, we identified 8 highly suitable SAACs for eNRR. Interestingly, the number of valence d-electrons in dopants proved crucial in screening for eNRR activity. These catalysts exhibited low activity in hydrogen evolution reaction, further enhancing their suitability for eNRR. This successful ML-driven approach accelerates catalyst design and discovery, holding significant practical implications.
环境条件下的电化学氮还原反应（eNRR）是一种很有前景的制氨（NH₃）方法，氨是肥料和化学品的关键前体，且无碳排放。单原子合金催化剂（SAACs）因其高活性、选择性和活性原子的高效利用而重振了催化过程。在此，我们采用密度泛函理论（DFT）计算与机器学习（ML）相结合的方法，研究基于十二面体纳米团簇的SAACs，以分析eNRR中的结构-活性关系。以所有过渡金属作为掺杂剂，筛选了300多种基于纳米团簇的SAACs，以建立一个预测稳定性和催化性能的ML模型。晶面位点被确定为最佳掺杂位置，特别是后期过渡金属表现出卓越的稳定性和活性。利用DFT+ML，我们确定了8种非常适合eNRR的SAACs。有趣的是，掺杂剂中的价d电子数在筛选eNRR活性方面被证明至关重要。这些催化剂在析氢反应中表现出低活性，进一步提高了它们对eNRR的适用性。这种成功的ML驱动方法加速了催化剂的设计和发现，具有重大的实际意义。
