Revolutionizing Catalyst Modeling with Structural Similarity Algorithms

Similarity Algorithm Graphic

Posted on Quantum Server Networks – June 2025

Understanding catalytic surface reactions at the atomic scale has long been the cornerstone of innovations in chemical and energy industries. Yet, modeling such complex systems—particularly multi-reactant adsorption on diverse surfaces—has remained a computationally expensive challenge. A new research breakthrough from the University of Rochester offers a transformative solution through a novel structural similarity algorithm that drastically reduces the need for costly simulations.

Why Modeling Multi-Reactant Catalysis is Challenging

First-principles methods like Density Functional Theory (DFT) are indispensable in computational materials science. However, simulating reactions involving multiple adsorbates on surfaces with varied geometries typically requires evaluating thousands of unique atomic configurations. This becomes a bottleneck when modeling complex electrocatalytic processes like CO and OH co-adsorption or hydrocarbon hydrogenation on platinum surfaces.

Introducing the Similarity Algorithm and Evo-Sim Framework

Published in the journal Chemical Science by Jin Zeng, Jiatong Gui, and Siddharth Deshpande (DOI: 10.1039/d5sc02117k), this study introduces a graph-theoretical similarity algorithm capable of quantifying structural differences between atomic configurations using eigenvalues of adjacency matrices representing chemical environments.

The similarity scores generated allow clustering of configurations and eliminate redundancy, ensuring that only the most distinct geometries are selected for DFT calculations. When combined with an energy-driven evolutionary algorithm, the resulting Evo-Sim workflow achieved an astounding 98% reduction in computational workload—without compromising accuracy.

Key Applications: CO–OH Co-adsorption and Ethylene Binding

The researchers applied the method to model 17 different coverage configurations of CO and OH on Pt(553) and Pt(111) surfaces, mapping out phase diagrams across varying electrochemical potentials. The results reaffirmed that Pt surfaces with undercoordinated step sites (like Pt(553)) exhibit superior CO oxidation activity at lower potentials compared to smoother Pt(111) surfaces.

In a second test case, the team modeled ethylene adsorption—a bidentate hydrocarbon important for hydrogenation reactions—across various coverages on Pt(553). The derived temperature-dependent phase diagrams matched experimental desorption data, validating the approach for hydrocarbon catalysis as well.

Scientific and Industrial Implications

This work represents a major step forward in accelerating computational catalyst design. The algorithm’s ability to minimize simulations while preserving insight will be invaluable for high-throughput screenings of new catalysts. Moreover, its flexibility makes it adaptable to alloys, defects, and solvent environments—crucial in real-world applications such as fuel cells, biomass upgrading, and CO2 reduction.

With code openly available on GitHub and data on Zenodo, the authors set a strong precedent for transparency and reproducibility in computational catalysis.

Conclusion

The introduction of a structural similarity algorithm, combined with an evolutionary search approach, presents a powerful new paradigm for modeling multi-reactant heterogeneous catalysis. This framework not only enhances computational efficiency but also unlocks new opportunities for rational catalyst discovery in complex reaction environments.

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

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#DFT #Catalysis #QuantumMaterials #MachineLearning #SurfaceScience #MaterialsDesign #Electrocatalysis #HeterogeneousCatalysis #COOxidation #PlatinumCatalyst

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