Efficient separation of C2H4/C2H6mixtures is of paramount importance in the petrochemical industry. Nanoporous materials, especially metal-organic frameworks (MOFs), may serve the purpose owing to their tailorable structures and pore geometries. In this work, we propose a computational framework for high-throughput screening and inverse design of high-performance MOFs for adsorption and membrane processes. High-throughput screening of the computational-ready, experimental (CoRE 2019) MOF database leads to materials with exceptionally high ethane-selective adsorption selectivity (LUDLAZ: 7.68) and ethene-selective membrane selectivity (EBINUA02: 2167.3). Moreover, the inverse design enables the exploration of broader chemical space and identification of MOF structures with even higher membrane selectivity and permeability. In addition, a relative membrane performance score (rMPS) has been formulated to evaluate the overall membrane performance relative to the Robeson boundary. The computational framework offers guidelines for the design of MOFs and is generically applicable to materials discovery for gas storage and separation.
Metal–organic frameworks (MOFs) are a class of nanoporous materials with highly tunable structures in terms of both chemical composition and topology. Due to their tunable nature, high‐throughput computational screening is a particularly appealing method to reduce the time‐to‐discovery of MOFs with desirable physical and chemical properties. In this work, a fully automated, high‐throughput periodic density functional theory (DFT) workflow for screening promising MOF candidates was developed and benchmarked, with a specific focus on applications in catalysis. As a proof‐of‐concept, we use the high‐throughput workflow to screen MOFs containing open metal sites (OMSs) from the Computation‐Ready, Experimental MOF database for the oxidative C—H bond activation of methane. The results from the screening process suggest that, despite the strong C—H bond strength of methane, the main challenge from a screening standpoint is the identification of MOFs with OMSs that can be readily oxidized at moderate reaction conditions. © 2019 Wiley Periodicals, Inc.
more » « less- NSF-PAR ID:
- 10461866
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Computational Chemistry
- Volume:
- 40
- Issue:
- 12
- ISSN:
- 0192-8651
- Page Range / eLocation ID:
- p. 1305-1318
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
MOF-encapsulated nanoparticles (NP@MOFs) are hybrid, heterogeneous catalysts, where the MOF could boost the activity and selectivity of the encapsulated NP for the reaction of choice by controlling reactant orientation. However, due to overwhelming combinatorics, methods to rapidly identify promising NP + MOF combinations for a given application are needed. Earlier work used a “surrogate” inert pore on top of NP-representative surfaces to generically capture MOF steric effects, hence enabling computational screening to focus on NP composition. However, the surrogate pore method neglects electronic effects of the MOF on the NP. Here, we use density functional theory to study how paradigmatic MOF linkers (imidazolate, carboxylate, and thiolate) impact the electronic structure of representative metal surfaces, and in turn the binding of small species, whose formation energies are commonly used in descriptor-based catalyst screening. We find that the coordinating moiety and functionalization of the linker modulates the shift in the metal d-band center and the electron transfer, which is correlated to experimentally measurable quantities such as C–O vibration frequencies. However, in the majority of cases, the effect of the linker on binding energies (for C*, O*, N*, H*, OH*, CH 3 * and CO*) was less than 10 kJ mol −1 . Furthermore, scaling relationships between binding energies were only slightly affected by linker-originated electronic effects. Therefore, activity/selectivity “heat maps” derived from calculations under “generic” steric constrains could remain useful to screen the optimal NP composition of an NP@MOF catalyst. On the other hand, the placement of a given NP composition on the aforementioned heat maps is affected by the MOF. For an n -butane oxidation case study, we estimated that Ag 3 Pd—a promising NP composition for selective 1-butanol formation according to previous screenings using the surrogate pore method—has a ∼85% probability of retaining a selectivity for 1-butanol above 75% when encapsulated in a carboxylic MOF of suitable pore size.more » « less
-
more » « less
Abstract This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.
-
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
-
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.more » « less
