Kim, Myungjoon Yeo, Byung Chul Park, Youngtae Lee, Hyuck Mo Han, Sang Soo Kim, Donghun 2024-01-19T18:30:17Z 2024-01-19T18:30:17Z 2021-09-05 2020-01-28 0897-4756 https://pubs.kist.re.kr/handle/201004/119070 The development of catalysts for the electrochemical N-2 reduction reaction (NRR) with a low limiting potential and high Faradaic efficiency is highly desirable but remains challenging. Here, to achieve acceleration, we develop and report a slab graph convolutional neural network (SGCNN), an accurate and flexible machine learning (ML) model that is suited for probing surface reactions in catalysis. With a self-accumulated database of 3040 surface calculations at the density-functional-theory (DFT) level, SGCNN predicted the binding energies, ranging over 8 eV, of five key adsorbates (H, N-2, N2H, NH, NH2) related to NRR performance with a mean absolute error (MAE) of only 0.23 eV. SGCNN only requires the low-level inputs of elemental properties available in the periodic table of elements and connectivity information of constituent atoms; thus, accelerations can be realized. Via a combined process of SGCNN-driven predictions and DFT verifications, four novel catalysts in the L1(2) crystal space, including V3Ir(111), Tc3Hf(111), V3Ni(111), and Tc3Ta(111), are proposed as stable candidates that likely exhibit both a lower limiting potential and higher Faradaic efficiency in the NRR, relative to the reference Mo(110). The ML work combined with a statistical data analysis reveals that catalytic surfaces with an average d-orbital occupation between 4 and 6 could lower the limiting potential and potentially overcome the scaling relation in the NRR. English AMER CHEMICAL SOC AMMONIA-SYNTHESIS ELECTROCHEMICAL SYNTHESIS ATMOSPHERIC-PRESSURE NITROGEN REDUCTION LOW-TEMPERATURE CO2 REDUCTION ADSORPTION ELECTROCATALYSTS SUPPRESSION MONOLAYER Artificial Intelligence to Accelerate the Discovery of N-2 Electroreduction Catalysts Article 10.1021/acs.chemmater.9b03686 1 CHEMISTRY OF MATERIALS, v.32, no.2, pp.709 - 720 CHEMISTRY OF MATERIALS 32 2 709 720 scie scopus 000510530500008 2-s2.0-85078295727 Chemistry, Physical Materials Science, Multidisciplinary Chemistry Materials Science Article AMMONIA-SYNTHESIS ELECTROCHEMICAL SYNTHESIS ATMOSPHERIC-PRESSURE NITROGEN REDUCTION LOW-TEMPERATURE CO2 REDUCTION ADSORPTION ELECTROCATALYSTS SUPPRESSION MONOLAYER Artificial intelligence Nitrogen reduction reaction Electrocatalyst Density functional theory Neural network