Machine learning high-throughput screening of rare earth SACs with different coordination environments for the HER

High-throughput screening of all rare earth SACs was conducted using ML to evaluate their HER performance. DFT calculated the Δ<i>G</i><sub>*H</sub> data of 100 groups of catalysts, and the model trained by the GBR algorithm exhibited the highest accuracy, with <i>R</i><sup>2</sup> and RMSE values of 0.970 and 0.157, respectively. The study also identified three potential HER catalysts (|Δ<i>G</i><sub>*H</sub>| < 0.20 eV).

Authors

Chao Liu (ORCID: https://orcid.org/0000-0002-0025-3636)
Qiming Fu (ORCID: https://orcid.org/0000-0001-9757-4323)
Wei Zhong (ORCID: https://orcid.org/0000-0001-6760-4157)
Meiling Liu (ORCID: https://orcid.org/0000-0002-1506-1143)
Shaik Gouse Peera (ORCID: https://orcid.org/0000-0002-2288-8800)

Institutions

Hunan Rare Earth Metal Material Research Institute (CN)
Jiangxi University of Science and Technology (CN)
Keimyung University (KR)

Publication Details

Journal: Chemical Communications
Published: 2025-11-25
DOI: https://doi.org/10.1039/d5cc05978j
Citations: 4
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 2.07

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article

Machine learning high-throughput screening of rare earth SACs with different coordination environments for the HER

Chao Liu, Qiming Fu, Wei Zhong, Meiling Liu et al.

Nov 25, 2025

4 citations

Chemical Communications

Machine Learning in Materials Science

2.07

article

Machine learning high-throughput screening of rare earth SACs with different coordination environments for the HER

Chao Liu, Qiming Fu, Wei Zhong, Meiling Liu, Shaik Gouse Peera

November 25, 2025article en

4 citations

Abstract

High-throughput screening of all rare earth SACs was conducted using ML to evaluate their HER performance. DFT calculated the Δ<i>G</i><sub>*H</sub> data of 100 groups of catalysts, and the model trained by the GBR algorithm exhibited the highest accuracy, with <i>R</i><sup>2</sup> and RMSE values of 0.970 and 0.157, respectively. The study also identified three potential HER catalysts (|Δ<i>G</i><sub>*H</sub>| < 0.20 eV).

Chemical CommunicationsVol. 62(2)

Hunan Rare Earth Metal Material Research Institute (CN), Jiangxi University of Science and Technology (CN), Keimyung University (KR)

Openalex Percentile: Top 12%

Machine Learning in Materials Science

2.07

DOI Source OpenAlex

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Machine learning high-throughput screening of rare earth SACs with different coordination environments for the HER — Chao Liu, Qiming Fu, et al. · Chemical Communications (2025) | TGRS Research Map | TGRS