In the exciting world of chemistry, researchers face a thrilling challenge: finding the perfect catalysts for speeding up important reactions. One such reaction, the Oxygen Evolution Reaction (OER), plays a crucial role in various processes in clean energy, such as generating green hydrogen, reducing nitrogen to make fertilizers, and even converting carbon dioxide to useful fuels. However, OER has a little quirk — it’s a bit sluggish, needing an extra energy push to get going. Catalysts aim to reduce the size of this push. So, how do scientists find catalysts that can unlock the full potential of the OER reaction? Enter Machine Learning (ML), a tool that is quickly becoming ubiquitous in our everyday lives and now holds the key to rapidly screening and discovering affordable and powerful catalysts.
Overcoming Challenges: The Need for Affordable and Durable High-Performing Catalysts
Iridium and ruthenium oxides are the current industrial go-to option for OER, but they come with a hefty price tag. Researchers are on a quest to find new catalysts that are not only cost-effective but also perform just as well as their expensive counterparts. However, there’s a twist — the harsh acidic environment of the commercial reactors in which the OER reaction takes place causes the catalysts to break down. The acidic environment is thus the kryptonite to our catalyst superheroes. New catalysts must be derived from materials that are affordable and capable of withstanding the harsh acidic environment, all without sacrificing too much of their performance.
The Superpower of High-Throughput DFT: Unleashing the Catalyst Hunt
To tackle the challenge, scientists have traditionally turned to a computational technique known as high-throughput Density Functional Theory (DFT), where thousands of computers solve powerful equations that provide us with information about material properties. Researchers have used this powerful tool to screen a vast array of potential catalysts, exploring countless combinations of metals and metal oxides. They looked for candidates that could outshine the expensive champions, iridium and ruthenium oxides while maintaining stability in the harsh acidic conditions.
The Battle of Computational Expense: Joining Forces with Machine Learning
Unfortunately, high-throughput DFT has an immense computational cost, making it challenging to explore the various combination of different metals and dopants therein. To overcome this challenge, researchers teamed up with a new ally — Machine Learning (ML). By training ML models with a fraction of the data generated by high-throughput DFT, researchers can predict the performance of the remaining catalysts, saving valuable time and computational resources.
Conclusion: The Dawn of a Catalyst Revolution
While the dream precious metal-free OER catalyst has so far remained elusive, the dynamic duo of high-throughput DFT and Machine Learning, equips scientists with tools to realize this dream in the near future. By combining the speed and efficiency of high-throughput DFT with the predictive abilities of ML, researchers can identify catalysts that possess extraordinary powers — the ability to perform superbly and remain stable in challenging environments. This collaboration between chemistry and computer science promises to revolutionize catalyst design and pave the way for groundbreaking advancements in renewable energy and beyond. So, let the hunt for catalyst superheroes begin!
References & Further Readings:
- Chatenet, M., Pollet, B. G., Dekel, D. R., Dionigi, F., Deseure, J., Millet, P., Braatz, R. D., Bazant, M. Z., Eikerling, M., Staffell, I., Balcombe, P., Shao-Horn, Y., & Schäfer, H. (2022). Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments. Chem. Soc. Rev., 51(11), 4583–4762. The Royal Society of Chemistry. https://doi.org/10.1039/D0CS01079K.
- Dinic, F., Singh, K., Dong, T., Rezazadeh, M., Wang, Z., Khosrozadeh, A., Yuan, T., Voznyy, O. (2021). Applied Machine Learning for Developing Next-Generation Functional Materials. Advanced Functional Materials. First published: 13 September 2021. https://doi.org/10.1002/adfm.202104195.
- Li, Z., Achenie, L. E. K., & Xin, H. (2020). An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts. ACS Catalysis, 10(7), 4377–4384. Publication Date: March 19, 2020. https://doi.org/10.1021/acscatal.9b05248.
- Zhong, M., Tran, K., Min, Y., Wang, C., Wang, Z., Dinh, C.-T., De Luna, P., Yu, Z., Rasouli, A. S., Brodersen, P., Sun, S., Voznyy, O., Tan, C.-S., Askerka, M., Che, F., Liu, M., Seifitokaldani, A., Pang, Y., Lo, S.-C., Ip, A., Ulissi, Z., & Sargent, E. H. (2020). Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature, 581(7807), 178–183. https://doi.org/10.1038/s41586-020-2242-8.
