Sena Aydin

Potential energy surfaces (PES) are an indispensable tool in the investigation, characterization and understanding of chemical and biological systems in the gas and condensed phases. Advances in machine learning (ML) methodologies have led to the development of ML-PES, which are now widely used to simulate such systems. This work provides an overview of concepts, methodologies and recommendations for constructing and using ML-PESs. The choice of topics is focused on the practical issues that are commonly found. Application of the principles discussed are illustrated through two different systems of biomolecular importance: the non-reactive dynamics of the Alanine-Lysine-Alanine tripeptide in gas and solution phases, and double proton transfer reactions in DNA base pairs.

The dynamics of lysozyme is probed by attaching –SCN to all alanine residues. The one-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption of 4 cm−1, which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution 12C → 13C leads to a redshift by −47 cm−1, which agrees quantitatively with experiments for CN-substituted copper complexes in solution. The low-frequency, far-infrared part of the protein spectra contains label-specific information in the difference spectra when compared with the wild type protein. Depending on the position of the labels, local structural changes are observed. For example, introducing the –SCN label at Ala129 leads to breaking of the α-helical structure with concomitant change in the far-infrared spectrum. Finally, changes in the local hydration of SCN-labeled alanine residues as a function of time can be related to the reorientation of the label. It is concluded that –SCN is potentially useful for probing protein dynamics, both in the high-frequency part (CN-stretch) and in the far-infrared part of the spectrum.

Mixed matrix membranes (MMMs) composed of two different fillers such as metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) embedded into polymers provide enhanced gas separation performance. Since it is not possible to experimentally consider all possible combinations of MOFs, COFs, and polymers, developing computational methods is urgent to identify the best performing MOF-COF pairs to be used as dual fillers in polymer membranes for target gas separations. With this motivation, we combined molecular simulations of gas adsorption and diffusion in MOFs and COFs with theoretical permeation models to calculate H₂, N₂, CH₄, and CO₂ permeabilities of almost a million types of MOF/COF/polymer MMMs. We focused on COF/polymer MMMs located below the upper bound due to their low gas selectivity for five industrially important gas separations, CO₂/N₂, CO₂/CH₄, H₂/N₂, H₂/CH₄, and H₂/CO₂. We further investigated whether these MMMs could exceed the upper bound when a second type of filler, a MOF, was introduced into the polymer. Many MOF/COF/polymer MMMs were found to exceed the upper bounds showing the promise of using two different fillers in polymers. Results showed that for polymers having a relatively high gas permeability (≥10⁴ barrer) but low selectivity (≤2.5) such as PTMSP, addition of the MOF as the second filler can have a dramatic effect on the final gas permeability and selectivity of the MMM. Property-performance relations were analyzed to understand how the structural and chemical properties of the fillers affect the permeability of the resulting MMMs, and MOFs having Zn, Cu, and Cd metals were found to lead to the highest increase in gas permeability of MMMs. This work highlights the significant potential of using COF and MOF fillers in MMMs to achieve better gas separation performances than MMMs with one type of filler, especially for H₂ purification and CO₂ capture applications.

Hundreds of covalent organic frameworks (COFs) have been synthesized, and thousands of them have been computationally designed. However, it is impractical to experimentally test each material as a membrane for gas separations. In this work, we focused on the membrane-based gas separation performances of experimentally synthesized COFs and hypothetical COFs (hypoCOFs). Gas permeabilities of COFs were computed by combining the results of grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, and many COF membranes were found to overcome the upper bound of polymeric membranes for He/H₂, N₂/CH₄, H₂/N₂, He/CH₄, H₂/CH₄, and He/N₂ separations. We then examined the structure-permeability relations of the COF membranes that are above the upper bound for each of the six gas separations, and based on these relations, we proposed an efficient approach for the selection of the best hypoCOFs from a very large database. Molecular simulations showed that 120 hypoCOFs that we identified to be promising based on these structure-performance relations exceed the upper bound for He/CH₄, He/N₂, H₂/CH₄, and H₂/N₂ separations. Both real and hypothetical COFs were then studied as fillers in 25 different polymers, leading to a total of 29 020 COF/polymer and hypoCOF/polymer mixed matrix membranes (MMMs), representing the largest number of COF-based MMMs investigated to date. Permeabilities and selectivities of COF/polymer MMMs were computed for six different gas separations, and results revealed that 18 of the 25 polymers can be carried above the upper bound when COFs were used as fillers. The comprehensive analysis of COFs provided in this work will fully unlock the potential of COF membranes and COF/polymer MMMs for helium separation and hydrogen purification.