Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Site Catalysts for CO<sub>2</sub> Reduction
Xuhao Wan, Zhaofu Zhang, Huan Niu, Yiheng Yin, Chunguang Kuai, Jun Wang, Chen Shao, Yuzheng Guo
Abstract
RR electrocatalyst with the limiting potential of only -0.33 V. The DFT-ML hybrid scheme accelerates the efficiency 6.87 times, while the prediction error is only 0.02 V, and it sheds light on the path to accelerate the rational design of efficient catalysts for energy conversion and conservation.
Topics & Concepts
CatalysisDensity functional theoryTransition metalNoble metalElectrocatalystChemistryRational designMaterials scienceReduction (mathematics)MetalInorganic chemistryElectrochemistryNanotechnologyComputational chemistryPhysical chemistryMathematicsOrganic chemistryGeometryElectrodeCO2 Reduction Techniques and CatalystsMachine Learning in Materials ScienceElectrocatalysts for Energy Conversion