Manara - Qatar Research Repository
Browse
1/1
3 files

Intelligent route to design efficient CO2 reduction electrocatalysts using ANFIS optimized by GA and PSO

journal contribution
submitted on 2023-12-25, 07:36 and posted on 2024-01-10, 08:02 authored by Majedeh Gheytanzadeh, Alireza Baghban, Sajjad Habibzadeh, Karam Jabbour, Amin Esmaeili, Amin Hamed Mashhadzadeh, Ahmad Mohaddespour

Recently, electrochemical reduction of CO2 into value-added fuels has been noticed as a promising process to decrease CO2 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 CO2 reduction.

Other Information

Published in: Scientific Reports
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1038/s41598-022-25512-8


The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Engineering and Technology - UDST
  • College of the North Atlantic - Qatar (-2022)
  • School of Engineering Technology and Industrial Trades - CNA-Q (-2022)

Usage metrics

    College of Engineering and Technology - UDST

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC