Intelligent route to design efficient CO 2 reduction electrocatalysts using ANFIS optimized by GA and PSO.

Autor: Gheytanzadeh M; Surface Reaction and Advanced Energy Materials Laboratory, Chemical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran., Baghban A; Chemical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Mahshahr Campus, Mahshahr, Iran. Alireza_baghban@alumni.ut.ac.ir., Habibzadeh S; Surface Reaction and Advanced Energy Materials Laboratory, Chemical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. sajjad.habibzadeh@mail.mcgill.ca., Jabbour K; College of Engineering and Technology, American University of the Middle East, Kuwait City, Kuwait., Esmaeili A; Department of Chemical Engineering, School of Engineering Technology and Industrial Trades, College of the North Atlantic - Qatar, Doha, Qatar., Mashhadzadeh AH; Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 010000, Nur-Sultan, Kazakhstan., Mohaddespour A; College of Engineering and Technology, American University of the Middle East, Kuwait City, Kuwait.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Dec 02; Vol. 12 (1), pp. 20859. Date of Electronic Publication: 2022 Dec 02.
DOI: 10.1038/s41598-022-25512-8
Abstrakt: Recently, electrochemical reduction of CO 2 into value-added fuels has been noticed as a promising process to decrease CO 2 emissions. The development of such technology is strongly depended upon tuning the surface properties of the applied electrocatalysts. Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. The developed ANFIS-PSO and ANFIS-GA showed excellent performance with RMSE of 0.0411 and 0.0383, respectively, the minimum errors reported so far in this field. Additionally, the sensitivity analysis showed that the center and the filling of the d-band are the most determining parameters for the electrocatalyst surface reactivity. The present study conveniently indicates the potential and value of machine learning in directing the experimental efforts in alloy system electrocatalysts for CO 2 reduction.
(© 2022. The Author(s).)
Databáze: MEDLINE
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