R. Goeminne

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.

Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations

R. Oktavian, R. Goeminne, L.T. Glasby, P. Song, R. Huynh, O. T. Qazvini, O. Ghaffari-Nik, N. Masoumifard, J. L. Cordiner, P. Hovington, V. Van Speybroeck, P. Z. Moghadam
Nature Communications
15, 3898
2024
A1

Abstract 

In 2021, Svante, in collaboration with BASF, reported successful scale up of CALF-20 production, a stable MOF with high capacity for post-combustion CO2 capture which exhibits remarkable stability towards water. CALF-20’s success story in the MOF commercialisation space provides new thinking about appropriate structural and adsorptive metrics important for CO2 capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure. We compare measured and predicted CO2 and water adsorption isotherms and explain the role of water-framework interactions and hydrogen bonding networks in CALF-20’s hydrophobic behaviour. Furthermore, regular and enhanced sampling molecular dynamics simulations are performed with both density-functional theory (DFT) and machine learning potentials (MLPs) trained to DFT energies and forces. From these simulations, the effects of adsorption-induced flexibility in CALF-20 are uncovered. We envisage this work would encourage development of other MOF materials useful for CO2 capture applications in humid conditions.

Gold Open Access

DFT-Quality Adsorption Simulations in Metal–Organic Frameworks Enabled by Machine Learning Potentials

R. Goeminne, L. Vanduyfhuys, V. Van Speybroeck, T. Verstraelen
Journal of Chemical Theory and Computation (JCTC)
19, 18, 6313-6325
2023
A1

Abstract 

Nanoporous materials such as metal–organic frameworks (MOFs) have been extensively studied for their potential for adsorption and separation applications. In this respect, grand canonical Monte Carlo (GCMC) simulations have become a well-established tool for computational screenings of the adsorption properties of large sets of MOFs. However, their reliance on empirical force field potentials has limited the accuracy with which this tool can be applied to MOFs with challenging chemical environments such as open-metal sites. On the other hand, density-functional theory (DFT) is too computationally demanding to be routinely employed in GCMC simulations due to the excessive number of required function evaluations. Therefore, we propose in this paper a protocol for training machine learning potentials (MLPs) on a limited set of DFT intermolecular interaction energies (and forces) of CO2 in ZIF-8 and the open-metal site containing Mg-MOF-74, and use the MLPs to derive adsorption isotherms from first principles. We make use of the equivariant NequIP model which has demonstrated excellent data efficiency, and as such an error on the interaction energies below 0.2 kJ mol–1 per adsorbate in ZIF-8 was attained. Its use in GCMC simulations results in highly accurate adsorption isotherms and heats of adsorption. For Mg-MOF-74, a large dependence of the obtained results on the used dispersion correction was observed, where PBE-MBD performs the best. Lastly, to test the transferability of the MLP trained on ZIF-8, it was applied to ZIF-3, ZIF-4, and ZIF-6, which resulted in large deviations in the predicted adsorption isotherms and heats of adsorption. Only when explicitly training on data for all ZIFs, accurate adsorption properties were obtained. As the proposed methodology is widely applicable to guest adsorption in nanoporous materials, it opens up the possibility for training general-purpose MLPs to perform highly accurate investigations of guest adsorption.

Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics

M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen, V. Van Speybroeck
Nature Communications
14, 1008
2023
A1

Abstract 

Proton hopping is a key reactive process within zeolite catalysis. However, the accurate determination of its kinetics poses major challenges both for theoreticians and experimentalists. Nuclear quantum effects (NQEs) are known to influence the structure and dynamics of protons, but their rigorous inclusion through the path integral molecular dynamics (PIMD) formalism was so far beyond reach for zeolite catalyzed processes due to the excessive computational cost of evaluating all forces and energies at the Density Functional Theory (DFT) level. Herein, we overcome this limitation by training first a reactive machine learning potential (MLP) that can reproduce with high fidelity the DFT potential energy surface of proton hopping around the first Al coordination sphere in the H-CHA zeolite. The MLP offers an immense computational speedup, enabling us to derive accurate reaction kinetics beyond standard transition state theory for the proton hopping reaction. Overall, more than 0.6 μs of simulation time was needed, which is far beyond reach of any standard DFT approach. NQEs are found to significantly impact the proton hopping kinetics up to ~473 K. Moreover, PIMD simulations with deuterium can be performed without any additional training to compute kinetic isotope effects over a broad range of temperatures.

Gold Open Access

Accurately Determining the Phase Transition Temperature of CsPbI3 via Random-Phase Approximation Calculations and Phase-Transferable Machine Learning Potentials

T. Braeckevelt, R. Goeminne, S. Vandenhaute, S. Borgmans, T. Verstraelen, J.A. Steele, M. Roeffaers, J. Hofkens, S.M.J. Rogge, V. Van Speybroeck
Chemistry of Materials
34, 19, 8561–8576
2022
A1

Abstract 

While metal halide perovskites (MHPs) have shown great potential for various optoelectronic applications, their widespread adoption in commercial photovoltaic cells or photosensors is currently restricted, given that MHPs such as CsPbI3 and FAPbI3 spontaneously transition to an optically inactive nonperovskite phase at ambient conditions. Herein, we put forward an accurate first-principles procedure to obtain fundamental insight into this phase stability conundrum. To this end, we computationally predict the Helmholtz free energy, composed of the electronic ground state energy and thermal corrections, as this is the fundamental quantity describing the phase stability in polymorphic materials. By adopting the random phase approximation method as a wave function-based method that intrinsically accounts for many-body electron correlation effects as a benchmark for the ground state energy, we validate the performance of different exchange-correlation functionals and dispersion methods. The thermal corrections, accessed through the vibrational density of states, are accessed through molecular dynamics simulations, using a phase-transferable machine learning potential to accurately account for the MHPs’ anharmonicity and mitigate size effects. The here proposed procedure is critically validated on CsPbI3, which is a challenging material as its phase stability changes slowly with varying temperature. We demonstrate that our procedure is essential to reproduce the experimental transition temperature, as choosing an inadequate functional can easily miss the transition temperature by more than 100 K. These results demonstrate that the here validated methodology is ideally suited to understand how factors such as strain engineering, surface functionalization, or compositional engineering could help to phase-stabilize MHPs for targeted applications.

Open Access version available at UGent repository
Gold Open Access

Charting the Complete Thermodynamic Landscape of Gas Adsorption for a Responsive Metal-Organic Framework

R. Goeminne, S. Krause, S. Kaskel, T. Verstraelen, J.D. Evans
JACS (Journal of the American Chemical Society)
143, 11, 4143–4147
2021
A1

Abstract 

New nanoporous materials have the ability to revolutionize adsorption and separation processes. In particular, materials with adaptive cavities have high selectivity and may display previously undiscovered phenomena, such as negative gas adsorption (NGA), in which gas is released from the framework upon an increase in pressure. Although the thermodynamic driving force behind this and many other counterintuitive adsorption phenomena have been thoroughly investigated in recent years, several experimental observations remain difficult to explain. This necessitates a comprehensive analysis of gas adsorption akin to the conformational free energy landscapes used to understand the function of proteins. We have constructed the complete thermodynamic landscape of methane adsorption on DUT-49. Traversing this complex landscape reproduces the experimentally observed structural transitions, temperature dependence, and the hysteresis between adsorption and desorption. The complete thermodynamic description presented here provides unparalleled insight into adsorption and provides a framework to understand other adsorbents that challenge our preconceptions.

Modeling Gas Adsorption in Flexible Metal–Organic Frameworks via Hybrid Monte Carlo / Molecular Dynamics Schemes

S.M.J. Rogge, R. Goeminne, R. Demuynck, J.J. Gutiérrez-Sevillano, S. Vandenbrande, L. Vanduyfhuys, M. Waroquier, T. Verstraelen, V. Van Speybroeck
Advanced Theory and Simulations
2 (4), 1800177
2019
A1

Abstract 

Herein, a hybrid Monte Carlo (MC)/molecular dynamics (MD) simulation protocol that properly accounts for the extraordinary structural flexibility of metal–organic frameworks (MOFs) is developed and validated. This is vital to accurately predict gas adsorption isotherms and guest‐induced flexibility of these materials. First, the performance of three recent models to predict adsorption isotherms and flexibility in MOFs is critically investigated. While these methods succeed in providing qualitative insight in the gas adsorption process in MOFs, their accuracy remains limited as the intrinsic flexibility of these materials is very hard to account for. To overcome this challenge, a hybrid MC/MD simulation protocol that is specifically designed to handle the flexibility of the adsorbent, including the shape flexibility, is introduced, thereby unifying the strengths of the previous models. It is demonstrated that the application of this new protocol to the adsorption of neon, argon, xenon, methane, and carbon dioxide in MIL‐53(Al), a prototypical flexible MOF, substantially decreases the inaccuracy of the obtained adsorption isotherms and predicted guest‐induced flexibility. As a result, this method is ideally suited to rationalize the adsorption performance of flexible nanoporous materials at the molecular level, paving the way for the conscious design of MOFs as industrial adsorbents.

Gold Open Access

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