Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material.

Jaehwan Lee, Seokwon Shin, Jaeho Lee, Young-Kyu Han, Woojin Lee, Youngdoo Son

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Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Jaehwan Lee *Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea.

Seokwon Shin *Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea.

Jaeho LeeDepartment of Energy and Materials Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea.

Young-Kyu HanDepartment of Energy and Materials Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea. ykenergy@dongguk.edu.

Woojin LeeSchool of AI Convergence, Dongguk University-Seoul, Seoul, 04620, South Korea. wj926@dgu.ac.kr.

Youngdoo SonDepartment of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea. youngdoo@dongguk.edu.

Funding

Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry 322050-3National Research Foundation of Korea 2022R1F1A1074393National Research Foundation of Korea RS-2023-00208412

6 · The paper itself

Abstract

Transition 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 ([Formula: see text]) 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.

Identifiers

PMID37543706

DOI10.1038/s41598-023-39696-0

PMCPMC10404247

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