Current Challenges in Catalysis

By Atif SuhailReviewed by Frances BriggsUpdated on Oct 21 2025

Catalysis is fundamental to chemical engineering, speeding up processes at large scales. Now, with the integration of powerful machine learning and earth-friendly materials, scientists are breaking away from slow trial-and-error methods and building catalytic systems that could reshape chemistry. 

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Catalysis research is entering a new era. While the last decade saw remarkable improvements in catalyst design, recent research emphasizes data-driven discovery and greener synthesis. From AI-designed enzymes to photocatalysts that replace rare metals, scientists are reimagining how we drive chemical reactions. 

Machine Learning and Computational Catalysis

Historically, catalyst design has been a slow, trial-and-error process. Small changes to a catalyst's structure can lead to dramatically altered performance, but predicting these effects in advance is near impossible with traditional tools. In the past, researchers have relied on these slower processes, synthesizing, testing, synthesizing, and testing again.

However, as machine learning has changed how we search the web, so it has changed R&D. AI models trained on chemical data can now spot patterns and correlations beyond human reach, helping researchers predict how new materials might behave before they're made.  

Recent work by Omranpour et al. demonstrates just how far this approach has come. Their machine learning potentials can simulate systems with thousands of atoms over nanoseconds, achieving nearly the same accuracy as density functional theory (DFT), a gold standard in chemistry. These models capture vast detail: tracking atomic positions, bond formation, charge transfer, and even solvent interactions at catalytic surfaces.¹ 

This means machine learning is increasingly being used not just as a screening tool, but to predict performance. Researchers are simulating real-world catalyst-solvent interfaces and mapping energy landscapes with increasing accuracy with these AI tools.¹

Computational techniques are adding an extra helping hand here. Stochastic surface walking (SSW) and active learning, for example, are aiding in automating the search for new reaction pathways. Combined with quantum-informed neural networks, these tools are enabling fully autonomous catalyst optimization, accelerating discoveries in CO₂ reduction, ammonia synthesis, and fuel cell development.¹   

Enzyme Engineering and Biocatalysis

Machine learning has made it even further, often being used in enzyme engineering research. Enzymes are natural biological catalysts, but are being genetically engineered to create custom, high-performing catalysts. Enzyme engineering modifies the genetics of microorganisms so they produce custom enzymes tailored for specific reactions. It's already used in pharmaceuticals and industrial bioprocessing, but designing enzymes that are stable and effective in harsh industrial environments remains a challenge. 

A recent work by Zhang et al. integrates AI-based stability predictions with large-scale protein language models to address these limitations. Their method helps in designing enzymes that work under extreme conditions, lilke high temperatures, organic solvents, or highly acidic environments.²

While engineered enzymes have previously been less stable under harsh conditions than their natural counterparts, these AI models are helping to close the gap. They're resulting in a growing field of machine learning-assisted biocatalysis, with applications that range from sustainable drug manufacturing to carbon recycling. 

Particularly promising are biohybrid catalysts, so called combinations of organic enzyme frameworks and inorganic materials. These hybrid systems blend molecular biology and materials science and are opening up further opportunities in chemical synthesis.²  

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Push to Replace Precious Metals

Possibly the biggest hindrance to catalytic processes is their widespread use of precious metals, such as cobalt and ruthenium in the Fischer-Tropsch method. These metals are scarce, expensive, and their extraction is environmentally costly too. 

One promising strategy for replacing these traditional catalytic techniques with their precious metals is photocatalysis. Accelerating reactions with light has long been studied, but is only just beginning to make its way into industrial methods.³

A collaboration between Princeton University, Rice University, and Syzygy Plasmonics Inc. has demonstrated this potential. The researchers used an iron-based photocatalyst to split ammonia into hydrogen under LED illumination. Previously, this reaction required ruthenium, but replacing it with abundant iron is a critical step toward more sustainable fuel cell production. 

In addition to hydrogen production, light-driven systems are also garnering attention for their ability to convert pollutants. Photothermal and singlet oxygen-based systems, for example, are being used to oxidize toxic materials and convert CO₂ under ambient conditions. These techniques indicate a shift toward photon-electron-phonon coupled catalysis, where light, heat, and charge transport work together to guide chemical reactions.^4-5

Catalytic Waste Management

It is no longer surprising that plastic waste is one of the most persistent environmental issues of our time. Traditional disposal methods, like landfills, incineration, and mechanical recycling, have well-documented downsides, and the global reduction in plastic use has been near zero. Catalysis could play a key role in finding clean alternatives.⁵ 

Photothermal catalysis, for instance, uses solar energy and heat to degrade pollutants and convert them into useful chemicals. A team led by Yu et al. recently demonstrated how this method can target volatile organic compounds and greenhouse gases with high efficiency.⁶

Singlet oxygen-based oxidation is also gaining momentum, particularly in air and water purification. This technique selectively breaks down organic pollutants with minimal by-products, making it attractive for wastewater treatment. 

Catalytic upcycling is another promising avenue. Xu et al. are one team of researchers who have developed processes to convert common plastics like polyolefins into fuels and fine chemicals through sequential hydrogenolysis and oxidation. With AI tools now being used to dynamically optimize catalyst-polymer interfaces, these reactions are being optimized even further for improved energy efficiency and selectivity.⁶ 

Next in Catalysis

The challenge in catalysis is no longer isolated to designing the most effective active sites. It's about orchestrating the movement of electrons, photons, and atoms across scales, from the molecular scale to industrial vats. That means building systems that are adaptive, data-driven, and capable of integrating multiple energy inputs simultaneously. 

Here, we report on a new manganese catalyst that could replace precious metals in solar and light catalysis!

References and Further Reading

1. Omranpour, A.; Elsner, J.; Lausch, K. N.; Behler, J., Machine Learning Potentials for Heterogeneous Catalysis. ACS Catalysis 2025, 15, 1616-1634.
2. Zhang, Y.; Moorhoff, F.; Qiu, S.; Dong, W.; Medina-Ortiz, D.; Zhao, J.; Davari, M. D., Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives. arXiv preprint arXiv:2507.07666 2025.
3. Yuan, Y.; Zhou, L.; Robatjazi, H.; Bao, J. L.; Zhou, J.; Bayles, A.; Yuan, L.; Lou, M.; Lou, M.; Khatiwada, S., Earth-Abundant Photocatalyst for H2 Generation from Nh3 with Light-Emitting Diode Illumination. Science 2022, 378, 889-893.
4. Qi, Z.; Wu, X.; Li, Q.; Lu, C.; Carabineiro, S. A.; Zhao, Z.; Liu, Y.; Lv, K., Singlet Oxygen in Environmental Catalysis: Mechanisms, Applications and Future Directions. Coordination Chemistry Reviews 2025, 529, 216439.
5. Yu, X.; Zhao, C.; Chen, Z.; Yang, L.; Zhu, B.; Fan, S.; Zhang, J.; Chen, C., Advances in Photothermal Catalysis for Air Pollutants. Chemical Engineering Journal 2024, 486, 150192.
6. Xu, S.; Tang, J.; Fu, L., Catalytic Strategies for the Upcycling of Polyolefin Plastic Waste. Langmuir 2024, 40, 3984-4000.

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