Abstract
Current study results on Machine Learning have been published. According to news reporting originating in Zhejiang, People's Republic of China, by NewsRx journalists, research stated, “Electrocatalytic nitrogen reduction reaction (NRR) has emerged as a sustainable and eco-friendly alternative for ammonia production at ambient conditions. Exploring highly efficient and selective electrocatalysts for NRR continues to gain significant attention, but remains a challenge.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Zhejiang Province. The news reporters obtained a quote from the research from Zhejiang Agriculture & Forestry University, “In this work, we conducted a series of single -atom catalysts (SACs) by embedding 29 kinds of transition metal ™ atoms on the two-dimensional hetero-geneous N-heterocyclic carbene, and systematically investi- gated their catalytic performance for NRR using density functional theory, high-throughput screening, and machine learning. Two promising candidates (TM = Mn and Ta) with high catalytic activity and selectivity were identified, with limiting potentials of -0.51 and -0.53 V, respectively. Moreover, considering solvation effects, the limiting potential for Mn was further reduced to-0.43 V. Machine learning (ML) analysis revealed that the adsorption energy of N2 emerged as an efficient descriptor for NRR activity, and transition metal atomic Mendeleev number (Nm), the molar volume of TM atoms (Vm) and the 1st ionization energy of TM atoms (Im) were intrinsic to the difference in NRR performance of these SACs.”
NETL
NSTL
Elsevier