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

ABSTRACT: 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 point

SUBMITTER: Gheytanzadeh M 

PROVIDER: S-EPMC9718738 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature