Functional porous metal–organic frameworks (MOFs) have been explored for a number of potential applications in catalysis, chemical sensing, water capture, gas storage, and separation. MOFs are among the most promising candidates to address challenges facing our society related to energy and environment, but the successful implementation of functional porous MOF materials are contingent on their stability; therefore, the rational design of stable MOFs plays an important role towards the development of functional porous MOFs. In this Focus article, we summarize progress in the rational design and synthesis of stable MOFs with controllable pores and functionalities. The implementation of reticular chemistry allows for the rational top-down design of stable porous MOFs with targeted topological networks and pore structures from the pre-selected building blocks. We highlight the reticular synthesis and applications of stable MOFs: (1) MOFs based on high valent metal ions ( e.g. , Al 3+ , Cr 3+ , Fe 3+ , Ti 4+ and Zr 4+ ) and carboxylate ligands; (2) MOFs based on low valent metal ions ( e.g. , Ni 2+ , Cu 2+ , and Zn 2+ ) and azolate linkers. We envision that the synthetic strategies, including modulated synthesis and post-synthetic modification, can potentially be extended to other more complex systems like metal-phosphonate framework materials.
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Graph Neural Network for Metal Organic Framework Potential Energy Approximation
Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal
ions and organic linkers. MOFs play an important role in industrial applications
such as gas separation, gas purification, and electrolytic catalysis. Important MOF
properties such a potential energy are currently computed via techniques such as
density functional theory (DFT). Although DFT provides accurate results, it is
computationally costly. We propose a machine learning approach for estimating
the potential energy of candidate MOFs, decomposing it into separate pair-wise
atomic interactions using a graph neural network. Such a technique will allow
high-throughput screening of candidates MOFs. We also generate a database of
50,000 spatial configurations and high quality potential energy values using DFT.
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- Award ID(s):
- 1940243
- NSF-PAR ID:
- 10314701
- Date Published:
- Journal Name:
- Machine Learning for Molecules Workshop at NeurIPS 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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 conductivity
k in 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. -
Polymeric membranes are being studied for their potential use in post-combustion carbon capture on the premise that they could dramatically lower costs relative to mature technologies available today. Mixed matrix membranes (MMMs) are advanced materials formed by combining polymers with inorganic particles. Using metal–organic frameworks (MOFs) as the inorganic particles has been shown to improve selectivity and permeability over pure polymers. We have carried out high-throughput atomistic simulations on 112 888 real and hypothetical metal–organic framework structures in order to calculate their CO 2 permeabilities and CO 2 /N 2 selectivities. The CO 2 /H 2 O sorption selectivity of 2 017 real MOFs was evaluated using the H 2 O sorption data of Li et al. (S. Li, Y. G. Chung and R. Q. Snurr, Langmuir , 2016, 32 , 10368–10376). Using experimentally measured polymer properties and the Maxwell model, we predicted the properties of all of the hypothetical mixed matrix membranes that could be made by combining the metal–organic frameworks with each of nine polymers, resulting in over one million possible MMMs. The predicted gas permeation of MMMs was compared to published gas permeation data in order to validate the methodology. We then carried out twelve individually optimized techno-economic evaluations of a three-stage membrane-based capture process. For each evaluation, capture process variables such as flow rate, capture fraction, pressure and temperature conditions were optimized and the resultant cost data were interpolated in order to assign cost based on membrane selectivity and permeability. This work makes a connection from atomistic simulation all the way to techno-economic evaluation for a membrane-based carbon capture process. We find that a large number of possible mixed matrix membranes are predicted to yield a cost of carbon capture less than $50 per tonne CO 2 removed, and a significant number of MOFs so identified have favorable CO 2 /H 2 O sorption selectivity.more » « less
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