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Open AccessDOI: 10.1016/S1872-5813(26)60693-7Original Research

A dataset for electrocatalytic hydrogen evolution reaction performance of non-noble transition metal phosphides

China University of Petroleum (Beijing)

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A dataset for electrocatalytic hydrogen evolution reaction performance of non-noble transition metal phosphides
Graphical Abstract / Figure
Published In
Journal of Fuel Chemistry and Technology
Published:January 15, 2026Edition:Vol. 54, Issue 7 • pp. 100-112Citation:ZHU Teng et al. (2026), Journal of Fuel Chemistry and Technology
Impact FactorPeer-Reviewed Core
Source Journal燃料化学学报

Key Takeaways & Executive Findings

  • • • The dataset comprises 203 non-noble transition metal phosphide catalysts, with performance metrics including overpotential at 10 mA/cm² (η10) and Tafel slope, enabling statistical benchmarking of HER activity. • • SHAP analysis identified phosphorus content, nickel content, and phosphidation temperature as the most critical features influencing HER performance, providing quantitative guidance for catalyst design. • • The dataset integrates preparation details, multi-element compositions, and testing conditions, allowing machine learning models to predict catalytic performance with validated effectiveness. • • The structured and standardized data resource supports rapid screening and rational development of high-performance HER catalysts, addressing the bottleneck of slow reaction kinetics in water electrolysis.

Abstract

This dataset compiles the hydrogen evolution reaction (HER) performance data of 203 non-noble transition metal phosphide (TMP) catalysts, covering detailed information on catalyst preparation (e.g., phosphating temperature, precursor, synthesis method), chemical composition (mass fractions of elements such as Ni, Co, Fe, P, Mo, W and Zn), and testing conditions (e.g., electrolyte type and concentration, electrode substrate). The key parameters for catalytic performance include the overpotential at 10 mA/cm2 (η10) and the Tafel slope. This dataset has been rigorously extracted, cleaned, and standardized to ensure a high degree of structure and machine readability. This provides a reliable data foundation for data-driven methods, such as machine learning and statistical modeling, enabling rapid screening and design of high-performance HER catalysts, supporting performance prediction, in-depth structure-activity analysis and the rational development of novel catalysts.

1. Introduction

Hydrogen production via water electrolysis is hindered by the sluggish kinetics of the hydrogen evolution reaction (HER), necessitating high-performance electrocatalysts. Noble metals like platinum set the performance benchmark but are scarce and costly, limiting large-scale application. Transition metal phosphides (TMPs) have emerged as promising non-noble alternatives due to their unique electronic restructuring, metallic-like conductivity, and corrosion resistance in acidic and alkaline media.

However, the vast parameter space of TMP synthesis and composition complicates catalyst optimization. This dataset addresses this bottleneck by systematically compiling HER performance data from 203 TMP catalysts, standardizing preparation details, elemental compositions, and testing conditions. By enabling data-driven analysis and machine learning, it accelerates the discovery of high-performance catalysts, offering a pathway to cost-effective green hydrogen production.

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Cite This Research Paper
ZHU Teng, WANG Huanhuan, LI Yuming (2026). A dataset for electrocatalytic hydrogen evolution reaction performance of non-noble transition metal phosphides. Journal of Fuel Chemistry and Technology. https://doi.org/10.1016/S1872-5813(26)60693-7
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Frequently Asked Questions

What are the most influential synthesis parameters affecting HER performance in TMP catalysts?

SHAP analysis identified phosphorus content, nickel content, and phosphidation temperature as the most critical features, quantitatively linking these parameters to overpotential and Tafel slope.

How does the dataset ensure reliability for machine learning applications?

The dataset was rigorously extracted, cleaned, and standardized, ensuring high structure and machine readability, which is essential for training predictive models.

What performance metrics are included and why are they important?

The overpotential at 10 mA/cm² (η10) and Tafel slope are included as they directly reflect catalytic activity and reaction kinetics, respectively, serving as benchmarks for catalyst comparison.

Can this dataset be used to predict HER performance of novel TMP compositions?

Yes, the dataset's structure and the validated machine learning models enable performance prediction for new catalyst formulations, accelerating the design of high-performance materials.

What is the scope of catalyst compositions covered in the dataset?

The dataset covers non-noble transition metal phosphides, including elements such as Ni, Co, Fe, P, Mo, W, and Zn, with mass fractions recorded for each.

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