Explaining the key challenge, Sreehari M S, a third-year PhD scholar in IITGN’s Department of Materials Engineering and first author of the study, said the interaction between hydrogen and a catalyst must remain balanced.
“If hydrogen binds too strongly, its release becomes difficult; if the interaction is too weak, the reaction cannot proceed efficiently. A hydrogen adsorption free energy close to zero is therefore desirable,” he said.
For comparison, the calculations placed pristine graphene at around +1.73 eV, while β-graphyne, the strongest-performing crystalline carbon material examined, recorded approximately +0.34 eV.
Importantly, the analysis found that bond distortions, irregular bond angles and stronger surface rippling were associated with more favourable hydrogen adsorption.
Conversely, regions retaining a more graphene-like structure showed less favourable catalytic characteristics. Dr Raghavan Ranganathan, Associate Professor in IITGN’s Department of Materials Engineering and Principal Investigator of the Computational Molecular Engineering Group, said the findings could offer a direction for designing future catalysts.
The researchers, however, stressed that the results are based on computational evidence and require experimental validation before the material’s practical hydrogen-production potential can be established.
Ashutosh Krishna Amaram, who contributed to the study and is currently pursuing doctoral research at the University of Illinois Chicago and Argonne National Laboratory, said machine learning enabled the team to investigate a large number of potential sites that would have been difficult to analyse using DFT alone.
The study used IITGN’s Param Ananta supercomputing facility for the simulations and could support future efforts to screen carbon-based materials before they are synthesised and experimentally tested.
The findings also align with the broader objectives of India’s National Green Hydrogen Mission, which seeks to expand domestic production and use of green hydrogen while improving the cost and efficiency of related technologies
