Ab Initio Derived Force Fields for Zeolitic Imidazolate Frameworks: MOF-FF for ZIFs

Dürholt, Johannes P.; Fraux, Guillaume; Coudert, François‐Xavier; Schmid, Rochus

doi:10.1021/acs.jctc.8b01041

Cited by 72 publications

(88 citation statements)

References 68 publications

(123 reference statements)

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“…[39] The DMOF-1 Zn 2 (bdc) 2 (dabco) system was described by the first principles parameterized MOF-FF force field. [40,41] Force field parameters were assigned automatically using our hierarchical automated parameter assignment (HAPA) strategy, [42] where building blocks are detected by substructure search based on the connectivity graph and force field parameters are automatically assigned from an SQLdatabase hosted at the MOF+ website (http://www.mofplus.org). As a reference for the NC simulations, a periodic model was also constructed as 2 × 2 × 2 supercell from system rotated by 45 • around the z-axis to maintain a mainly orthorhombic unit cell during transformation.…”

Section: Methods and Computational Detailsmentioning

confidence: 99%

Molecular Dynamics Simulations of the “Breathing” Phase Transformation of MOF Nanocrystallites

Keupp

Schmid

2019

Advcd Theory and Sims

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Due to an error in the implementation of the analysis routine that extracts the average total potential energy ⟨U⟩ from the trajectory data, the reported values for ΔU are too small by a constant factor of 7 in the case of the periodic simulations in this manuscript. Due to this constant factor, Figures 5b and 6b are essentially identical, with, however, a scaled ordinate. The corrected figures are shown in Figure 1 and 2. As a consequence, the derived entropy contributions −TΔS to the free energy ΔA for the periodic reference case were also incorrect in the published article. The corrected Figure 6c of the original manuscript is now given in Figure 3. Due to the negligible temperature dependence of ΔU, the entropy contribution is still the dominating factor that destabilizes the cp phase at higher temperature as concluded in the original manuscript. The erroneous values for ΔU cp−op , ΔU ‡ cp−op , ΔS cp−op reported in Table S1 in the supporting information have been corrected in Table 1 and volumes for the cp and op phases were added. The Column ΔS ‡ was removed due to the absence of an entropic barrier in the corrected data.

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Section: Methods and Computational Detailsmentioning

confidence: 99%

Molecular Dynamics Simulations of the “Breathing” Phase Transformation of MOF Nanocrystallites

Keupp

Schmid

2019

Advcd Theory and Sims

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“…Also, generic force fields like DREIDING were used to model ZIF-8. [455] Recently, more general and transferable intra-molecular ZIF force fields were developed by Verploegh et al [456] , ZIF-FF by Weng et al [457] and MOF-FF for ZIFs by Durholt et al [458] Several other general approaches and force field for MOFs have been developed. MOF-FF, a third generation force field of Schmid and coworkers is based on the building block methodology.…”

Section: Mof Force Fieldsmentioning

confidence: 99%

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Dubbeldam

Walton

Vlugt

et al. 2019

Advcd Theory and Sims

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Molecular simulations are an excellent tool to study adsorption and diffusion in nanoporous materials. Examples of nanoporous materials are zeolites, carbon nanotubes, clays, metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and zeolitic imidazolate frameworks (ZIFs). The molecular confinement these materials offer has been exploited in adsorption and catalysis for almost 50 years. Molecular simulations have provided understanding of the underlying shape selectivity, and adsorption and diffusion effects. Much of the reliability of the modeling predictions depends on the accuracy and transferability of the force field. However, flexibility and the chemical and structural diversity of MOFs add significant challenges for engineering force fields that are able to reproduce experimentally observed structural and dynamic properties. Recent developments in design, parameterization, and implementation of force fields for MOFs and zeolites are reviewed.contain silicon and oxygen. They are readily available, very stable, and relatively cheap. The material should have the right combination of high adsorption selectivity, combined with adequate capacity for use in fixed-bed devices. Recently, new classes of nanoporous materials have been designed that have stability, high void volumes, and well defined tailorable cavities of uniform size. Example are metal-organic frameworks (MOFs), [1][2][3][4][5][6][7] covalent organic frameworks (COFs), [8,9] and zeolitic imidazolate frameworks (ZIFs). [10] These novel materials posses almost unlimited structural variety because of the many combinations of building blocks that can be imagined. The building blocks self-assembly during synthesis into crystalline materials that, after evacuation of the structure, can find applications in adsorption separations, air purification, gas storage, chemical sensing, and catalysis. [4,11] The judicious selection of building blocks allows the pore volume and functionality to be tailored in a rational manner. Because of the designprinciple underlying the synthesis, it is important to understand structure-properties relations, such as the mechanisms of gas separation as a function of shape and size of the pore system, in order to create "blueprints to MOF-design." Computer simulation is cost-effective, fast, and allows for rapid identification of important structural parameters affecting adsorption.Most of the published works on molecular simulation studying zeolites have used the Kiselev model. [12] In this model, zeolite atoms are fixed and interactions of guest atoms with silicon atoms is accounted for by an effective interaction only with the surrounding oxygen atoms. Interactions between sorbate molecules and the host are modeled by placing Lennard-Jones sites and partial charges on all framework atoms and sorbate molecules. The Kiselev model is attractive because of its simplicity and computational efficiency. This model has shown significant success, both in using the molecular dynamics method to compute, for example, the diffu...

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“…The tendency of optimizers to get trapped in a local minimum will be tested by restarting the fitting procedure from the same initial guess (with different random seeds) or from different random initial parameter vectors. Since GAs are also popular for the parametrization of other force fields, [47][48][49][50][51][52][53] we expect that the comparison in this work is also useful beyond the scope of ReaxFF. 34 Key properties of the three training sets are compared in Table 1, highlighting fundamental differences in the types of information they contain.…”

Section: Introductionmentioning

confidence: 99%

ReaxFF Parameter Optimization with Monte Carlo and Evolutionary Algorithms: Guidelines and Insights

Shchygol¹,

Yakovlev²,

Trnka³

et al. 2019

Preprint

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ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, i.e. Monte-Carlo Force Field optimizer (MCFF) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). In this work, CMA-ES, MCFF and a GA method (OGOLEM) are systematically compared using three training sets from the literature. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two third of the cases. For each method, we provide reasonable default settings and our analysis offers useful guidelines for their usage in future work. An important side effect impairing the parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, e.g. by using exclusively unambiguous geometry optimizations in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts.<br>

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Ab Initio Derived Force Fields for Zeolitic Imidazolate Frameworks: MOF-FF for ZIFs

Cited by 72 publications

References 68 publications

Molecular Dynamics Simulations of the “Breathing” Phase Transformation of MOF Nanocrystallites

Molecular Dynamics Simulations of the “Breathing” Phase Transformation of MOF Nanocrystallites

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

ReaxFF Parameter Optimization with Monte Carlo and Evolutionary Algorithms: Guidelines and Insights

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