首页|Data on Machine Learning Discussed by Researchers at Guizhou Normal University (The Rational Co-doping Strategy of Transition Metal and Non-metal Atoms On G-cn for Highly Efficient Hydrogen Evolution By Dft and Machine Learning)
Data on Machine Learning Discussed by Researchers at Guizhou Normal University (The Rational Co-doping Strategy of Transition Metal and Non-metal Atoms On G-cn for Highly Efficient Hydrogen Evolution By Dft and Machine Learning)
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NETL
NSTL
Elsevier
Researchers detail new data in Machine Learning. According to news reporting originating from Guiyang, People's Republic of China, by NewsRx correspondents, research stated, "As a clean energy source, hydrogen has attracted high interest in developing efficient hydrogen evolution reaction (HER) catalysts due to its sustainable and renewable characteristics. In this work, we have systematically investigated the HER activity of the g-CN two-dimensional materials." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Guizhou Provincial Basic Research Program (Natural Science), Top scientific and technological talents in Guizhou Province, Guizhou Normal University Academic New Talent Fund, Guizhou Normal New Talent, Functional Materials and Devices Technology Innovation Team of Guizhou Province University. Our news editors obtained a quote from the research from Guizhou Normal University, "The catalytic activity in HER is enhanced by doping non-metallic atoms (C, N, B, Si) and transition metal atoms (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) in the vacancies of g-CN. Based on the first principle calculation, we screened 40 structures and found that the Delta G(H*) of Sc@C-3-CN, V@C-3-CN, Mn@C-3-CN, Sc@N-3- CN, and Ti@Si-3-CN was close to zero. Among them, Ti@Si-3-CN has the lowest Gibbs free energy change (-0.01 eV) and has excellent HER performance. In addition, we explored HER activity's origin by using machine learning (ML) algorithms."
GuiyangPeople's Republic of ChinaAsiaCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine LearningGuizhou Normal University
NETL
NSTL
Elsevier
