H. J. Mahanta

Interpretable machine learning reveals geometry-dominated design rules for atmospheric CO2 capture in metal-organic frameworks.

Direct air capture (DAC) requires sorbents capable of binding CO2 at a partial pressure of 0.0004 bar, where conventional flue-gas screening criteria may not define absorption performance. Machine-learning (ML) surrogates for Grand Canonical Monte Carlo (GCMC) simulation can accelerate screening of metal-organic framew...