More Like this

1. Active Learning for Adsorption Simulations: Evaluation, Criteria Analysis, and Recommendations for Metal–Organic Frameworks

   https://doi.org/10.1021/acs.iecr.3c01589  

   Osaro, Etinosa; Mukherjee, Krishnendu; Colón, Yamil J. (August 2023, Industrial & Engineering Chemistry Research)

   High-throughput molecular simulations and machine learning (ML) have been implemented to adequately screen a large number of metal−organic frameworks (MOFs) for applications involving adsorption. Grand canonical Monte Carlo (GCMC) simulations have proven effective in calculating the adsorption capacity at given pressures and temperatures, but they can require expensive computational resources. While they can be resource-efficient, ML models can require large datasets, creating a need for algorithms that can efficiently characterize adsorption; active learning (AL) can play a very important role in this regard. In this work, we make use of Gaussian process regression (GPR) to model pure component adsorption of nitrogen at 77 K from 10−5 to 1 bar, methane at 298 K from 10 −5 to 100 bar, carbon dioxide at 298 K from 10−5 to 100 bar, and hydrogen at 77 K from 10−5 to 100 bar on PCN-61, MgMOF-74, DUT-32, DUT-49, MOF-177, NU-800, UiO-66, ZIF-8, IRMOF-1, IRMOF-10, and IRMOF-16. The GPR model requires an initial training of the model with an initial dataset, the prior one, and, in this study of evaluating AL, we make use of three different prior selection schemes. Each prior scheme is updated with a sampling point resulting from the GP model uncertainties. This protocol continues until a maximum GPR relative error of 2% is attained. We make a recommendation on the best prior selection scheme for the total 44 adsorbate−adsorbent pairs primarily making use of the mean absolute error and the total amount of points required for convergence of the model. To further evaluate the AL framework, we apply the BET consistency criteria on the simulated and GP nitrogen isotherms and compare the resulting surface areas. 

   more » « less
2. High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity

   https://doi.org/10.1038/s41524-022-00961-x  

   Islamov, Meiirbek; Babaei, Hasan; Anderson, Ryther; Sezginel, Kutay B.; Long, Jeffrey R.; McGaughey, Alan J. H.; Gomez-Gualdron, Diego A.; Wilmer, Christopher E. (January 2023, npj Computational Materials)

   Abstract Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivitykin MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm⁻³), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m⁻¹ K⁻¹), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m⁻¹ K⁻¹), the structures of which we describe in additional detail. 

   more » « less
3. Exploiting hydrophobicity and hydrophilicity in nanopores as a design principle for “smart” MOF microtanks for methane storage

   https://doi.org/10.1039/C9ME00072K  

   Anderson, Ryther; Seong, Bomsaerah; Peterson, Zöe; Stevanak, Molly; Carreon, Moises A.; Gómez-Gualdrón, Diego A. (January 2020, Molecular Systems Design & Engineering)

   Widespread use of methane-powered vehicles likely requires the development of efficient on-board methane storage systems. A novel concept for methane storage is the nanoporous microtank, which is based on a millimeter-sized nanoporous pellet (the core) surrounded by an ultrathin membrane (the shell). Mixture adsorption simulations in idealized pores indicate that by combining a pellet that features large, hydrophobic pores with a membrane featuring small, hydrophilic pores, it would be possible to trap a large amount of “pressurized” methane in the pellet while keeping the external pressure low. The methane would be trapped by sealing the surrounding membrane with the adsorption of a hydrophilic compound such as methanol. Additional simulations in over 2000 hypothesized metal–organic frameworks (MOFs) indicate that the above design concept could be exploited using real nanoporous materials. Structure–property relationships derived from these simulations indicate that MOFs suitable for the core (storing over 250 cc(STP) CH4 per cc) should have a pore size in the 12–14 Å range and linkers without appreciably hydrophilic moieties. On the other hand, MOFs suitable for the shell should have a pore size less than 9 Å and linkers with hydrophilic functional groups such as –CN, –NO 2 , –OH and –NH 2 . Simulation snapshots suggest that the hydrogen bonding between these groups and hydrophilic moieties of methanol would be critical for the sealing function. 

   more » « less
4. Solvent-Directed Assembly of π-Stacked 3D Metal–Organic Frameworks with Tunable Conductivity Enhanced by C ₆₀ Encapsulation

   https://doi.org/10.1021/jacs.5c04897  

   Tang, Xianhui; Wang, Xijun; Su, Shengyi; Wang, Xiaoliang; Xie, Haomiao; Li, Timothy Y-Z; Gong, Wei; Jia, Lei; Du, Enping; Xie, Ke; et al (June 2025, Journal of the American Chemical Society)

   Metal-organic frameworks (MOFs) with tunable structures and unique host-guest chemistry have emerged as promising candidates for conductive materials. However, the tunability of conductivity and porosity in conductive MOFs and their interrelationship still lack a systematic study. Herein, we report the synthesis of a series of 3D copper MOFs (NU-4000 to NU-4003) using a triphenylene-based hexatopic carboxylate linker. By modulating the ratio of mixed solvents, distinct structural topologies and π-π stacking arrangements were achieved, resulting in electrical conductivity ranging from insulators (˂ 10-6 S/cm) to semiconductors (10-8 ~ 102 S/cm). Among them, NU-4003 features continuous π-π stacking and exhibits a conductivity of 1.7 × 10-6 S/cm. To further enhance conductivity, we encapsulated C60, a strong electron acceptor, within the circular channels of NU-4003, resulting in a remarkable conductivity increase to 140 S/cm with approximately 100% pore occupancy. Even at lower C60 loadings that leave 54% of the pore volume remaining accessible, the conductivity remains exceptionally high at 104 S/cm. This represents an eight-order magnitude enhancement and positions NU-4003-C60 as one of the most conductive 3D MOFs reported to date. This work integrates two charge transport pathways (through-space and electron donor and acceptor) into a single MOF host-guest material, achieving a significant enhancement in conductivity. This study demonstrates the potential of combining host-guest chemistry and π-π stacking to design conductive MOFs with permanent porosity maintained, providing a blueprint for the development of next-generation materials for electronic and energy-related applications. 

   more » « less
5. Sequential design of adsorption simulations in metal–organic frameworks

   https://doi.org/10.1039/D1ME00138H  

   Mukherjee, Krishnendu; Dowling, Alexander W.; Colón, Yamil J. (December 2021, Molecular Systems Design & Engineering)

   The large number of possible structures of metal–organic frameworks (MOFs) and their limitless potential applications have motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF performance. Some of the techniques include large-scale high-throughput molecular simulations and machine learning models. Despite those advances, the number of possible materials and the potential conditions that could be used still pose a formidable challenge for model development requiring large data sets. Therefore, there is a clear need for algorithms that can efficiently explore the spaces while balancing the number of simulations with prediction accuracy. Here, we present how active learning can sequentially select simulation conditions for gas adsorption, ultimately resulting in accurate adsorption predictions with an order of magnitude lower number of simulations. We model adsorption of pure components methane and carbon dioxide in Cu–BTC. We employ Gaussian process regression (GPR) and use the resulting uncertainties in the predictions to guide the next sampling point for molecular simulation. We outline the procedure and demonstrate how this model can emulate adsorption isotherms at 300 K from 10 −6 to 300 bar (methane)/100 bar (carbon dioxide). We also show how this procedure can be used for predicting adsorption on a temperature–pressure phase space for a temperature range of 100 to 300 K, and pressure range of 10 −6 to 300 bar (methane)/100 bar (carbon dioxide). 

   more » « less