Machine Learning Maps 10,000 Reactions to Reveal How Copper Turns CO2 into Fuel

One of the most tantalizing promises in modern chemistry is the idea that carbon dioxide, the waste gas driving climate change, could be recycled into methanol and other fuels on an industrial scale. The catch has always been complexity. When CO2 meets hydrogen on the surface of a catalyst, thousands of individual bond-breaking and bond-forming events unfold in parallel, and no experimental technique can watch them all in real time. For decades, computational chemists have tried to fill that gap with quantum-mechanical simulations, but the cost of calculating every possible reaction has forced them to study only a handful of plausible steps at a time. Now, researchers at the Indian Institute of Science (IISc) in Bengaluru have shown what happens when that restriction is lifted, and their findings overturn a long-standing assumption about how hydrogen behaves on a catalyst surface.

The team, led by Ananth Govind Rajan, Associate Professor in the Department of Chemical Engineering at IISc, constructed a data-driven computational framework that maps nearly 10,000 elementary chemical reactions involved in converting CO2 into fuels and chemicals over a copper catalyst. The study, published in Nature Communications, demonstrates that a small, hand-picked reaction network can lead a model spectacularly astray, while a comprehensive, machine-learning-accelerated network brings predictions into line with laboratory reality. The work offers a template that could extend well beyond CO2 hydrogenation to other catalytic processes central to a sustainable energy economy.

The scale of the problem is easy to underestimate. In CO2 hydrogenation, CO2 reacts with hydrogen over a catalyst to yield products such as methanol and carbon monoxide, but each product emerges from a tangled web of intermediates adsorbed on the metal surface. Traditional mechanistic modelling relies on chemical intuition to select perhaps a few dozen likely reactions, because modeling even one reaction with methods such as density functional theory demands enormous computational resources. That intuition, the IISc researchers argued, carries a hidden risk: the network might quietly omit the one step that actually controls the outcome.

First author Anand Mohan Verma, who carried out the work as a CV Raman Postdoctoral Fellow at IISc and is currently an Assistant Professor at Motilal Nehru National Institute of Technology Allahabad, framed the central dilemma of the field. Mechanistic modellers can never be certain that a curated reaction network has not excluded the critical step, and that uncertainty undermines confidence in every downstream prediction. The new framework was designed specifically to eliminate that blind spot by replacing intuition with systematic enumeration.

The researchers began by generating a carefully curated database of 152 reactions using quantum-mechanical simulations, establishing a foundation of reliable energetics. They then trained machine learning models to rapidly predict activation energy barriers for additional reactions, borrowing the pattern-recognition strengths of modern artificial intelligence to sidestep the expense of calculating each barrier from first principles. In parallel, they deployed automated enumeration tools to identify every possible single-step reaction involving 105 distinct surface species. Combining these approaches expanded the original seed network to 9,389 elementary reactions, a catalog several orders of magnitude larger than anything a conventional modelling effort would attempt.

The payoff was immediate and dramatic. When the team modeled CO2 hydrogenation using only the initial 152 reactions, the network predicted that formic acid, not methanol, would emerge as the dominant product, and it badly underestimated how much CO2 would be converted overall. Only after expanding the network to include the thousands of previously overlooked reactions did the predictions align with experimental observations from the researchers’ own work and from published studies. Rajan emphasized that this discrepancy shows how small networks can produce qualitatively wrong answers while appearing perfectly rigorous on their own terms.

Plugged into a kinetic model, the expanded network predicted an approximately 40-fold increase in CO2 conversion compared with the sparse network, and it correctly identified methanol and carbon monoxide as the major products, matching what experiments actually deliver. Experimental validation was performed by collaborators G Valavarasu and Santhosh Kotni at Hindustan Petroleum Corporation Limited’s Green Research and Development Centre, and by Amol Amrute and colleagues at the Agency for Science, Technology, and Research in Singapore. The machine learning components drew on expertise from Ambedkar Dukkipati, Professor in the Department of Computer Science and Automation at IISc, underscoring the interdisciplinary character of the project.

The most conceptually striking result concerned hydrogen transfer. The enlarged network revealed that in several key steps, hydrogen can be delivered to reaction intermediates directly as intact molecular H2, rather than only through individual hydrogen atoms dissociated on the surface. Quantum-mechanical calculations performed explicitly on those steps confirmed that this molecular pathway can be particularly favourable for hydrogen transfer to oxygen-containing intermediates. This finding challenges a foundational assumption taught in catalysis courses, namely that H2 must first split into atoms before it can participate in surface chemistry, and it emerged purely because the network was large enough for the pathway to appear.

Shivam Chaturvedi, a PhD student in the Department of Chemical Engineering at IISc and co-author of the study, noted that the molecular hydrogen transfer pathway surfaced only because the reaction network was sufficiently expansive, and that the observation held up when the team returned to compute those specific steps with full quantum-mechanical rigor. The finding also carries practical implications for catalyst design. Catalysts that interact more strongly with H2 could potentially enhance the pathways leading to methanol, offering a concrete design principle for engineers seeking to improve the yield of this valuable fuel and chemical feedstock from CO2.

Beyond its immediate insights into copper-catalyzed CO2 hydrogenation, the framework represents a general strategy for exploring complex catalytic chemistry. By combining quantum-mechanical simulations for ground-truth energetics, machine learning for rapid interpolation across vast reaction spaces, automated enumeration for exhaustive coverage, and kinetic modelling for connection to observable outcomes, the approach can in principle be applied to other industrially important processes, including CO2 reduction on alternative catalysts, nitrogen reduction for ammonia synthesis, and water splitting for green hydrogen production. As decarbonization efforts intensify worldwide, tools that can map reaction landscapes comprehensively, rather than selectively, may prove decisive in turning atmospheric carbon into useful chemistry at scale.

Subject of Research: Data-driven computational mapping of catalytic CO2 hydrogenation reaction networks on copper

Article Title: Using machine learning and computation to unveil mechanisms for CO2-to-fuel conversion

Article References: Using machine learning and computation to unveil mechanisms for CO2-to-fuel conversion. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: CO2 hydrogenation, machine learning, copper catalyst, methanol synthesis, reaction networks, computational catalysis, density functional theory, kinetic modelling, catalyst design, hydrogen transfer, Nature Communications, carbon recycling