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Databases of experimentally-derived metal–organic framework (MOF) crystal structures are useful for large-scale computational screening to identify which MOFs are best-suited for particular applications. However, these crystal structures must be cleaned to identify and/or correct various artifacts. The recently published 2019 CoRE MOF database (Chung et al. , J. Chem. Eng. Data , 2019, 64 , 5985–5998) reported thousands of experimentally-derived crystal structures that were partially cleaned to remove solvent molecules, to identify hundreds of disordered structures (approximately thirty of those were corrected), and to manually correct approximately 100 structures ( e.g. , adding missing hydrogen atoms). Herein, further cleaning of the 2019 CoRE MOF database is performed to identify structures with misbonded or isolated atoms: (i) structures containing an isolated atom, (ii) structures containing atoms too close together ( i.e. , overlapping atoms), (iii) structures containing a misplaced hydrogen atom, (iv) structures containing an under-bonded carbon atom (which might be caused by missing hydrogen atoms), and (v) structures containing an over-bonded carbon atom. This study should not be viewed as the final cleaning of this database, but rather as progress along the way towards the goal of someday achieving a completely cleaned set of experimentally-derived MOF crystal structures. We performed atom typing for all of the accepted structures to identify those structures that can be parameterized by previously reported forcefield precursors (Chen and Manz, RSC Adv ., 2019, 9 , 36492–36507). We report several forcefield precursors ( e.g. , net atomic charges, atom-in-material polarizabilities, atom-in-material dispersion coefficients, electron cloud parameters, etc. ) for more than five thousand MOFs in the 2019 CoRE MOF database.
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A collection of forcefield precursors for metal–organic frameworks
A host of important performance properties for metal–organic frameworks (MOFs) and other complex materials can be calculated by modeling statistical ensembles. The principle challenge is to develop accurate and computationally efficient interaction models for these simulations. Two major approaches are (i) ab initio molecular dynamics in which the interaction model is provided by an exchange–correlation theory ( e.g. , DFT + dispersion functional) and (ii) molecular mechanics in which the interaction model is a parameterized classical force field. The first approach requires further development to improve computational speed. The second approach requires further development to automate accurate forcefield parameterization. Because of the extreme chemical diversity across thousands of MOF structures, this problem is still mostly unsolved today. For example, here we show structures in the 2014 CoRE MOF database contain more than 8 thousand different atom types based on first and second neighbors. Our results showed that atom types based on both first and second neighbors adequately capture the chemical environment, but atom types based on only first neighbors do not. For 3056 MOFs, we used density functional theory (DFT) followed by DDEC6 atomic population analysis to extract a host of important forcefield precursors: partial atomic charges; atom-in-material (AIM) C 6 , C 8 , and C 10 dispersion coefficients; AIM dipole and quadrupole moments; various AIM polarizabilities; quantum Drude oscillator parameters; AIM electron cloud parameters; etc. Electrostatic parameters were validated through comparisons to the DFT-computed electrostatic potential. These forcefield precursors should find widespread applications to developing MOF force fields.
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- Award ID(s):
- 1555376
- PAR ID:
- 10123994
- Date Published:
- Journal Name:
- RSC Advances
- Volume:
- 9
- Issue:
- 63
- ISSN:
- 2046-2069
- Page Range / eLocation ID:
- 36492 to 36507
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/c9ra07327b
