Author:
Lee Jaehwan,Shin Seokwon,Lee Jaeho,Han Young-Kyu,Lee Woojin,Son Youngdoo
Abstract
AbstractTransition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properties, such as catalytic activity, have been shown to have limitations in their dependence on large amounts of training data and massive computations. Herein, we propose a genetic descriptor search that efficiently identifies a set of descriptors through a genetic algorithm, without requiring intensive calculations. We conducted both quantitative and qualitative experiments on a total of 70 TMDs to predict hydrogen adsorption free energy ($$\Delta G_H$$
Δ
G
H
) with the generated descriptors. The results demonstrate that the proposed method significantly outperformed the feature extraction methods that are currently widely used in machine learning applications.
Funder
National Research Foundation of Korea
Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry
Publisher
Springer Science and Business Media LLC