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Updated: Jun 16, 2026

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance
Published on: April 27, 2018
Inverse design and discovery of high entropy alloy catalysts for efficient oxygen evolution reaction
Jyotishraj Thoudam1, Ankit Jain1
1Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India.
High-entropy alloys (HEAs) offer a cost-effective alternative to iridium dioxide for oxygen evolution reactions (OERs). Machine learning identified MnCoNiCuZn as a promising HEA catalyst, though further optimization is needed for performance comparable to iridium dioxide.
Area of Science:
- Materials Science
- Electrochemistry
- Computational Materials Science
Background:
- High-entropy alloys (HEAs) composed of earth-abundant elements are potential replacements for expensive catalysts like iridium dioxide (IrO2) in oxygen evolution reactions (OERs).
- The vast compositional space and complex structure-property relationships of HEAs present significant challenges for catalyst development.
Purpose of the Study:
- To utilize machine learning frameworks to discover novel HEA catalysts for efficient OER applications.
- To identify HEA compositions with optimal OH adsorption energy as a descriptor for catalytic performance.
Main Methods:
- Employed machine learning-based regression models to screen over 163 x 10^9 HEA configurations.
- Selected 5 elements (Mn, Fe, Co, Ni, Cu, Zn, Mo) for HEA design, focusing on OER activity.
- Utilized a computational hydrogen electrode model to assess the catalytic activity of the designed HEAs in an OER environment.
Main Results:
- Identified MnCoNiCuZn HEA with a specific stoichiometric ratio (2:2:8:12:12) as having the highest likelihood for favorable OH binding energy.
- Calculated theoretical overpotential for MnCoNiCuZn was 0.67 V, with explicit calculations showing deviations up to 0.99 V.
- Comparative analysis showed rutile IrO2 (η = 0.33 V) outperformed the MnCoNiCuZn catalyst (η = 0.57 V) in OER performance.
Conclusions:
- The developed machine learning methodology successfully identified statistically favorable adsorption sites within a complex compositional space.
- While MnCoNiCuZn showed potential, further refinement is necessary to match the performance of established catalysts like IrO2.
- This approach provides a viable strategy for discovering highly active HEA electrocatalysts for OER.
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