IISc researchers use machine learning and computation to unveil mechanisms for CO2-to-fuel conversion

The new approach by the institute to develop a data-driven computational framework could help scientists better understand and eventually design effective catalysts for turning CO₂ into chemicals and fuels. | Photo Credit: Getty Images

Researchers at the Indian Institute of Science (IISc) have developed a data-driven computational framework that maps nearly 10,000 chemical reactions involved in converting carbon dioxide (CO₂) hydrogenation into fuels and chemicals on a copper catalyst.

According to IISc, scientists are increasingly exploring CO₂ hydrogenation, where CO₂ reacts with hydrogen over a catalyst to give rise to products like methanol and convert it into useful chemicals and fuels. However, this process involves thousands of tiny chemical steps happening on the catalyst surface.

“To model these steps computationally, researchers traditionally select a relatively small number of likely reactions, as modelling every possible reaction using quantum mechanics is prohibitively expensive. But this means that hundreds of important reactions may be missed,” IISc said.

New approach

However, this new approach by the institute to develop a data-driven computational framework could help scientists better understand and eventually design effective catalysts for turning CO₂ into chemicals and fuels.