Machine-learning enables nitrogen reduction reaction on transition metal doped C<sub>3</sub>B by controlling the charge states
Chengwei Yang, Chao Yang, Yunxia Liang, Hongxia Yan, Aodi Zhang, Guixian Ge, Wentao Wang, Pengfei Ou
Abstract
The charge transfer and d-band center are key factors affecting the NRR performance of TM@C 3 B, both of which can be modulated by charge states. Thus, use of charge states in modeling electrochemical reactions represents a new design method.
Topics & Concepts
DopingMaterials scienceNitrogenCharge (physics)State (computer science)Reduction (mathematics)Transition metalEngineering physicsInorganic chemistryNanotechnologyChemistryComputer scienceOptoelectronicsCatalysisPhysicsOrganic chemistryMathematicsAlgorithmQuantum mechanicsGeometryMachine Learning in Materials Science