Ai-Driven High Throughput Screening (HTC) Approaches to Overcoming the Challenges of Electrocatalysis for Hydrogen Evolution Reaction (HER): A Review

The urgent need for sustainable hydrogen production has intensified research into efficient electrocatalysts for the hydrogen evolution reaction (HER), yet challenges like the high cost of platinum-group metals (PGMs) and catalyst degradation persist. This review explores how AI-driven high-throughput screening (HTS) accelerates the discovery of low-cost, durable HER electrocatalysts by bridging computational predictions and experimental validation. We analyze recent advancements where machine learning (ML) models—trained on density functional theory (DFT) datasets and experimental metrics—predict key descriptors (e.g., ΔG_H, d-band center) to identify non-precious alternatives like Ni₃Mo (ΔG_H ≈ 0.08 eV) and CoMoS₄ (overpotential = 32 mV). Autonomous laboratories equipped with robotic synthesis platforms further expedite material testing, exemplified by the discovery of La₀.₅Sr₀.₅CoO₃ via a self-driving lab that screened 1,200 perovskites. Despite progress, limitations such as data scarcity and the "black box" nature of ML hinder broader adoption. We highlight strategies to enhance interpretability, including explainable AI (XAI) techniques like SHAP values, which reveal atomic-level insights (e.g., pyrrolic-N dopants in Fe-N₄ SACs). Multi-objective optimization (MOO) frameworks balance activity, stability, and cost, while active learning loops refine predictions iteratively. Challenges like overfitting (RMSE > 0.2 eV for small datasets) and synthesis bottlenecks for complex morphologies are critically evaluated. The review concludes with recommendations: open-access databases for standardized HER data, physics-informed ML to integrate mechanistic equations, and operando characterization to capture dynamic catalyst behavior. By addressing these gaps, AI-HTS can unlock scalable, economically viable HER catalysts, advancing the global transition to green hydrogen.

AI-driven high-throughput screening, hydrogen evolution reaction, electrocatalysis

1. Chen, Y. (2023). Deep neural networks for predicting hydrogen adsorption energies in transition metal alloys. ACS Catalysis, 13(5), 4567–4578. [Google Scholar] [Crossref]

2. Chen, Y., Wang, L., & Zhang, Q. (2023). Open-source databases for accelerating electrocatalyst discovery. Joule, 7(4), 892–905. [Google Scholar] [Crossref]

3. Gomez, A., Liu, T., & Schmidt, J. (2024). Explainable AI for rational catalyst design: Bridging the gap between prediction and mechanism. Nature Catalysis, 7(1), 45–58. [Google Scholar] [Crossref]

4. Gupta, A., & Lee, S. (2023). Machine learning-driven design of corrosion-resistant Ni-based electrocatalysts for hydrogen evolution. Advanced Energy Materials, 14(8), 2300123. [Google Scholar] [Crossref]

5. Johnson, R. T., Smith, K., & Patel, M. (2022). Convolutional neural networks for predicting surface restructuring in MoS₂ electrocatalysts. Nature Communications, 13(1), 789. [Google Scholar] [Crossref]

6. Kim, H., Park, J., & Nguyen, T. (2024). Autonomous discovery of perovskite oxide catalysts via robotic high-throughput screening and deep learning. Science Robotics, 9(85), 7866. [Google Scholar] [Crossref]

7. Kumar, R., Lee, J., & Kim, H. (2022). Surface oxidation dynamics of non-precious HER catalysts under industrial operating conditions. ACS Applied Materials & Interfaces, 14(22), 25329–25341. [Google Scholar] [Crossref]

8. Lee, X. (2020). Accelerated screening of MXenes for hydrogen evolution using machine learning. Advanced Materials, 32(45), 2003421. [Google Scholar] [Crossref]

9. Li, X., Chen, Z., & Wang, H. (2021). Operando characterization-guided machine learning for dynamic HER catalysis. Advanced Functional Materials, 31(18), 2008991. [Google Scholar] [Crossref]

10. Nguyen, T. H., Park, S., & Lee, K. (2024). Degradation mechanisms of platinum catalysts in PEM electrolyzers: An AI-driven analysis. Energy & Environmental Science, 17(2), 567–579. [Google Scholar] [Crossref]

11. Nguyen, V. Q., & Rivera, D. (2023). Bayesian optimization of WS₂@MoS₂ heterostructures for enhanced hydrogen evolution. Nano Energy, 98, 107321. [Google Scholar] [Crossref]

12. Park, J., Kim, Y., & Singh, R. (2023). AI-enhanced operando Raman spectroscopy for monitoring HER catalyst evolution. ACS Sensors, 8(5), 1899–1908. [Google Scholar] [Crossref]

13. Patel, S., & Thompson, L. (2023). Interpretable machine learning for multi-objective optimization of CoP-based HER catalysts. Journal of Materials Chemistry A, 11(14), 7765–7776. [Google Scholar] [Crossref]

14. Rivera, D., & Singh, R. (2024). Attention-based neural networks for interpretable design of single-atom catalysts. Joule, 8(2), 456–472. [Google Scholar] [Crossref]

15. Singh, R., & Patel, S. (2024). Sustainable HER catalysis: Integrating life-cycle assessment with machine learning. Green Chemistry, 26(3), 1324–1336. [Google Scholar] [Crossref]

16. Smith, P., & Zhou, Y. (2023). Economic barriers to global hydrogen scaling: A techno-economic analysis. Energy Policy, 181, 113702. [Google Scholar] [Crossref]

17. Thompson, L., & Johnson, R. (2022). Transfer learning for predicting catalytic activity in data-scarce regimes. ACS Applied Materials & Interfaces, 14(30), 34221–34230. [Google Scholar] [Crossref]

18. Thompson, L., Rivera, D., & Gomez, A. (2023). Visualizing active sites in MoS₂ catalysts using explainable AI. Nano Letters, 23(6), 2178–2185. [Google Scholar] [Crossref]

19. Wang, Q., Zhang, Y., & Chen, Y. (2022). Active learning-driven discovery of CoMoS₄ as a high-performance hydrogen evolution catalyst. Energy & Environmental Science, 15(3), 1203–1214. [Google Scholar] [Crossref]

20. Zhang, Y. (2021). Machine learning in electrocatalysis: From fundamentals to applications. Advanced Science, 8(12), 2100953. [Google Scholar] [Crossref]

21. Zhang, Y., Wang, Q., & Chen, Y. (2023). Robotic synthesis of biomass-derived carbon catalysts for sustainable HER. Advanced Sustainable Systems, 7(7), 2200498. [Google Scholar] [Crossref]

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