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Hydrogen Evolution Reaction: Unraveling the Factors Behind the Efficiency of Hydrogen Evolution in Endohedrally Doped C 60 Structures via Ab Initio Calculations and Insights from Machine Learning Models (Adv. Theory Simul. 3/2019)
Author(s) -
Tahini Hassan A.,
Tan Xin,
Smith Sean C.
Publication year - 2019
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201970008
Subject(s) - ab initio , ab initio quantum chemistry methods , hydrogen , computational chemistry , doping , density functional theory , chemistry , materials science , computer science , physics , quantum mechanics , molecule
Machine learning has emerged as a powerful complementation to accurate ab intio methods. In article number 1800202 , Sean C. Smith and co‐workers have studied C 60 metallofullerenes as catalysts for hydrogen evolution reactions. By using a number of electronic features, metallofullerenes' overpotentials can be rapidly assessed without fully resorting to time consuming ab initio calculations.