Machine learning accelerated discovery of low-Pt high entropy intermetallic compounds for electrochemical oxygen reduction reaction

Aug 3, 2026

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Advancing cathode catalyst design aims to minimize platinum usage while improving catalyst durability for fuel cells. This work demonstrates a PtM3-type low-Pt high-entropy intermetallic as an efficient electrocatalyst for oxygen reduction reaction. Crystal graph convolutional neural networks accelerate composition design, using a first-principles dataset to reach very low errors for surface strain and formation energy. A predicted PtFe0.75Co0.75Ni0.75Cu0.75 material is synthesized into ~6 nm nanoparticles, delivering strong mass/specific activity and enhanced stability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

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Machine Learning Accelerated Electrocatalyst Discovery

Platinum Minimization and Oxygen Reduction Reaction Background

Pt-Based Intermetallic Catalysts and Motivation for PtM3

Limitations: Stability and Thermodynamics of Low-Pt Intermetallics

High-Entropy Intermetallics as a Strategy

CGCNN-Based Composition Design and Model Training

This is the peer reviewed version of the following article: Zhang, L. , Zhang, X. , Chen, C. , Zhang, J. , Tan, W. , Xu, Z. , Zhong, Z. , Du, L. , Song, H. , Liao, S. , Zhu, Y. , Zhou, Z. , & Cui, Z. (2024) . Machine Learning-Aided Discovery of Low-Pt High Entropy Intermetallic Compounds for Electrochemical Oxygen Reduction Reaction. Angewandte Chemie International Edition, 63(51), e202411123, which has been published in final format [https://doi.org/](https://doi.org/)[https://doi.org/10.1002/anie.202411123. This article may be](https://doi.org/10.1002/anie.202411123. This article may be) used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. Machine learning accelerated discovery of low-Pt high entropy intermetallic compounds for electrochemical oxygen reduction reaction Longhai Zhang‡, \#, Jiaxi Zhang‡, \#, Changsheng Chen⊥, \#, Weiquan Tan‡, Xu Zhang∇, Li Du‡, Huiyu Song‡, Shijun Liao‡, Ye Zhu⊥, * and Zhiming Cui‡, * ‡The Key Laboratory of Fuel Cell Technology of Guangdong Province, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China. ⊥Department of Applied Physics, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hong Kong, 999077, China. ∇School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, China. \#These authors contributed equally to this work. KEYWORDS: Machine learning; low-Pt; high entropy intermetallic compounds; oxygen reduction reaction ABSTRACT: Advancing the design of novel cathode catalysts to significantly minimize platinum utilization and augment the longevity of the catalyst has emerged as a formidable challenge in the field of fuel cells. Here, the PtM3 type low-Pt high entropy intermetallic (HEI) with ultra-high efficiency is first demonstrated as a class of advanced electrocatalyst for oxygen reduction reaction. Machine learning techniques using crystal graph convolutional neural networks (CGCNN) was employed to expedite the composition design, which can hardly be achieved by massive trail-and-error efforts. By training the CGCNN model on a dataset generated from first-principles calculations, a high accuracy with a mean absolute error (MAE) of 0.002 for surface strain and 0.282 eV for formation energy is achieved. Further, the HEI PtFe0.75Co0.75Ni0.75Cu0.75 with PtM3 type structure was predicted to be a prossing ORR catalyst and was successfully synthesied with nanoparticle sizes around 6 nm, which demonstrates a mass activity of 4.09 Amg Pt-1 and a specific activity of 7.92 mA cm-2, as well as significantly enhanced stability. Discovery of advanced electrocatalysts for oxygen reduction reaction (ORR) is crucial for the development of proton exchange membrane fuel cells (PEMFCs) .1 Because the ORR has a four-protoncoupled electron transfer process, it usually suffers from a high over potential to overcome its kinetic barrier.2-3 Pt/C is still the state of art ORR catalyst but high Pt loading on membrane is needed to provide enough power density because of its limited activity. The scarce resources and increased cost of Pt have led to extensive research to reduce the amount of Pt in a catalyst without compromising its performance.4 Structurally ordered Pt-based intermetallic compounds (ICs) have recently evolved as a class of promising ORR catalysts due to their well-defined crystal structure, stoichiometry, improved c

FAQs

What problem does the document address in oxygen reduction reaction electrocatalysts?

It targets reducing platinum utilization and improving catalyst longevity for oxygen reduction reaction in fuel cells, where Pt/C has limitations due to high Pt loading needs.

How does the document use machine learning in the discovery process?

Crystal graph convolutional neural networks (CGCNN) are trained on a dataset generated from first-principles calculations to accelerate composition design instead of relying on trial-and-error experiments.

What catalyst and performance metrics are reported after prediction?

The predicted PtFe0.75Co0.75Ni0.75Cu0.75 (PtM3 type) is synthesized with nanoparticle sizes around 6 nm, showing mass activity of 4.09 A mg Pt-1 and specific activity of 7.92 mA cm-2, along with enhanced stability.

Aug 3, 2026

Upload by Rowan