Unravelling Composition–Activity–Stability Trends in High Entropy Alloy Electrocatalysts by Using a Data-Guided Combinatorial Synthesis Strategy and Computational Modeling

Authors: Lars Banko, Olga A. Krysiak, Jack K. Pedersen, Bin Xiao, Alan Savan, Tobias Löffler, Sabrina Baha, Jan Rossmeisl, Wolfgang Schuhmann, and Alfred Ludwig

Published in: Advanced Energy Materials (2022)

DOI: 10.1002/aenm.202103312

As global demand for sustainable energy solutions grows, the search for advanced catalytic materials becomes more critical than ever. In their groundbreaking study, researchers from Ruhr University Bochum and the University of Copenhagen have made significant strides in this direction by exploring the vast potential of High Entropy Alloys (HEAs) as electrocatalysts.

What Are High Entropy Alloys?

HEAs are metallic materials composed of five or more elements in roughly equal proportions. Unlike traditional alloys, their unique structure allows for unprecedented tunability of properties such as stability, activity, and selectivity—traits that are crucial for applications in hydrogen evolution, CO2 reduction, and oxygen reduction reactions (ORR).

The Challenge

The enormous number of possible element combinations in HEAs results in a vast and largely unexplored compositional space. Traditional trial-and-error approaches are inefficient in navigating this complexity. To overcome this, the researchers developed a combinatorial synthesis strategy, paired with computational modeling, to systematically explore this space.

The Approach: Combinatorial Material Libraries

The team focused on a five-metal system: Ru–Rh–Pd–Ir–Pt. Using magnetron co-sputtering, they fabricated six thin-film materials libraries (MLs), each with a unique permutation of the target arrangement. This method allowed them to create over 2000 unique sample compositions and explore up to 41% of the HEA composition space, significantly outperforming conventional single-sample methods.

Key Findings

• Best Performing Composition: The highest ORR activity was observed for the composition Ru₍₂₅₎Rh₍₁₅₎Pd₍₃₁₎Ir₍₁₅₎Pt₍₁₄₎, outperforming even the best binary systems.

• Deviations from Theory: Experimental data revealed surface atom arrangements that differ from those predicted by theoretical models, highlighting the importance of integrating real-world data into simulations.

• High-Throughput Advantage: The study demonstrated the power of high-throughput experimentation in identifying composition–activity–stability relationships across a five-dimensional space.

A New Path Forward

This research represents a significant advancement in the field of materials discovery. By combining experimental data with predictive modeling, the team was able to refine theoretical assumptions and provide insights into the real behavior of complex alloy surfaces. Their work not only identifies promising new catalysts but also sets a foundation for data-guided innovation in material science.

Conclusion

High Entropy Alloys offer a promising pathway toward the next generation of highly active and stable electrocatalysts. This study proves that data-driven, combinatorial approaches are not just beneficial—they are essential for unlocking their full potential. As we move toward a more sustainable energy future, such methodologies will be key to accelerating innovation.