S. Ravichandran

Reaching quantum accuracy in predicting adsorption properties for ethane/ethene in ZIF-8 at the low pressure regime

S. Ravichandran, M. Najafi, R. Goeminne, J. F. M. Denayer, V. Van Speybroeck, L. Vanduyfhuys
Journal of Chemical Theory and Computation
2024
A1

Abstract 

Nanoporous materials in the form of metal−organicframeworks such as zeolitic imidazolate framework-8 (ZIF-8) arepromising membrane materials for the separation of hydrocarbonmixtures. To compute the adsorption isotherms in suchadsorbents, grand canonical Monte Carlo simulations have provento be very useful. The quality of these isotherms depends on theaccuracy of adsorbate−adsorbent interactions, which are mostlydescribed using force fields owing to their low computational cost.However, force field predictions of adsorption uptake often showdiscrepancies from experiments at low pressures, providing theneed for methods that are more accurate. Hence, in this work, wepropose and validate two novel methodologies for the ZIF-8/ethane and ethene systems; a benchmarking methodology toevaluate the performance of any given force field in describing adsorption in the low-pressure regime and a refinement procedure torescale the parameters of a force field to better describe the host−guest interactions and provide for simulation isotherms with closeagreement to experimental isotherms. Both methodologies were developed based on a reference Henry coefficient, computed withthe PBE-MBD functional using the importance sampling technique. The force field rankings predicted by the benchmarkingmethodology involve the comparison of force field derived Henry coefficients with the reference Henry coefficients and ranking theforce fields based on the disparities between these Henry coefficients. The ranking from this methodology matches the rankingsmade based on uptake disparities by comparing force field derived simulation isotherms to experimental isotherms in the low-pressure regime. The force field rescaling methodology was proven to refine even the worst performing force field in UFF/TraPPE.The uptake disparities of UFF/TraPPE improved from 197% and 194% to 11% and 21% for ethane and ethene, respectively. Theproposed methodology is applicable to predict adsorption across nanoporous materials and allows for rescaled force fields to reachquantum accuracy without the need for experimental input.

High-Throughput Screening of Covalent Organic Frameworks for Carbon Capture Using Machine Learning

J. De Vos, S. Ravichandran, S. Borgmans, L. Vanduyfhuys, P. Van der Voort, S.M.J. Rogge, V. Van Speybroeck
Chemistry of Materials
36, 9, 4315-4330
2024
A1

Abstract 

Postcombustion carbon capture provides a high-potential pathway to reduce anthropogenic CO2 emissions in the short term. In this respect, nanoporous materials, such as covalent organic frameworks (COFs), offer a promising platform as adsorbent beds. However, due to the modular nature of COFs, an almost unlimited number of structures can possibly be synthesized. To efficiently identify promising materials and reveal performance trends within the COF material space, we present a computational high-throughput screening of 268,687 COFs for their ability to efficiently and selectively separate CO2 from the flue gas of power plants using a pressure swing adsorption process. Furthermore, we demonstrate that our screening can be significantly accelerated using machine learning to identify a set of promising materials. These are subsequently characterized with high accuracy, taking into account the effects of competitive adsorption and coadsorption. Our screening reveals that imide, (keto)enamine, and (acyl)hydrazone COFs are particularly interesting for carbon capture. Additionally, the best-performing materials are 3D COFs possessing strong CO2 adsorption sites between aromatic rings at opposite sides of pores with a diameter of 1.0 nm. In 2D COFs, a significant influence of the framework chemistry, such as imide linkages or fluoro groups, is observed. Our design rules can guide experimental researchers to construct high-performing COFs for CO2 capture.

Gold Open Access

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