Ab initio derived force fields for Zeolitic Imidazolate Frameworks: MOF-FF for ZIFs

10.26434/chemrxiv.7322183.v1 ◽

2018 ◽

Author(s):

Johannes P. Dürholt ◽

Guillaume Fraux ◽

François-Xavier Coudert ◽

Rochus Schmid

Keyword(s):

Force Field ◽

Ab Initio ◽

Dynamic Properties ◽

Evolutionary Strategy ◽

Continuous Variables ◽

Zeolitic Imidazolate Frameworks ◽

Dynamic Simulations ◽

Zeolitic Imidazolate Framework ◽

Covariance Matrix Adaptation ◽

Global Optimizer

<div> <div> <div> <p>In this paper we parameterized in a consistent way a new force field for a range of different zeolitic imidazolate framework systems (ZIF-8, ZIF-8(H), ZIF-8(Br) and ZIF- 8(Cl)), extending the MOF-FF parameterization methodology in two aspects. First, we implemented the possibility to use periodic reference data in order to prevent the difficulty of generating representative finite clusters. Second, a more efficient global optimizer based on the covariance matrix adaptation evolutionary strategy (CMA-ES) was employed during the parameterization process. We confirmed that CMA-ES, as a state-of-the-art black box optimizer for problems on continuous variables, is more suitable for force field optimization than the previous genetic algorithm. The obtained force field was then fully validated with respect to static and dynamic properties. Much effort was spent to ensure that the FF is able to describe the crucial linker swing effect in a large number of ZIF-8 derivatives. For this reason we compared our force field to ab initio molecular dynamic simulations and found an accuracy comparable to those obtained by different exchange–correlation functionals. </p></div></div></div><div><div><div> </div> </div> </div>

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Flexible and Transferable ab Initio Force Field for Zeolitic Imidazolate Frameworks: ZIF-FF

The Journal of Physical Chemistry A ◽

10.1021/acs.jpca.8b12311 ◽

2019 ◽

Vol 123 (13) ◽

pp. 3000-3012 ◽

Cited By ~ 6

Author(s):

Tingting Weng ◽

J. R. Schmidt

Keyword(s):

Force Field ◽

Ab Initio ◽

Zeolitic Imidazolate Frameworks

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

10.26434/chemrxiv.6606668.v2 ◽

2019 ◽

Author(s):

Ganna Shchygol ◽

Alexei Yakovlev ◽

Tomáš Trnka ◽

Adri C.T. van Duin ◽

Toon Verstraelen

Keyword(s):

Monte Carlo ◽

Force Field ◽

Parameter Optimization ◽

Optimal Parameter ◽

Global Optimum ◽

Evolutionary Strategy ◽

Computationally Efficient ◽

Training Set ◽

Covariance Matrix Adaptation ◽

Numerical Noise

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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Systematic Comparison of Monte Carlo Annealing and Covariance Matrix Adaptation for the Optimization of ReaxFF Parameters

10.26434/chemrxiv.6606668.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ganna Shchygol ◽

Alexei Yakovlev ◽

Tomáš Trnka ◽

Adri C.T. van Duin ◽

Toon Verstraelen

Keyword(s):

Monte Carlo ◽

Force Field ◽

Covariance Matrix ◽

General Pattern ◽

Computational Cost ◽

Evolutionary Strategy ◽

Optimal Parameters ◽

Training Set ◽

Covariance Matrix Adaptation ◽

Training Sets

<div>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 calibrated 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), which have the potential to find good parameters at a relatively low computational cost. In this work, these two methods are tested, as implemented in ADF2018, using three ReaxFF training sets, which have previously been used to benchmark GAs. Even though MCFF and CMA-ES should not be considered as exhaustive global optimizers, they can find parameters that are comparable in quality to those obtained with GAs. We observe that CMA-ES leads to slightly better results and is less sensitive to the initial guess of the parameters. Concrete recipes are provided for obtaining similar results with new training sets.</div><div>Besides optimization recipes, a successful ReaxFF parameterization requires the design of a good training set. At every trial set of parameters, ReaxFF is used to optimize molecular geometries in the training set. When the optimization of some geometries fails easily, it becomes increasingly difficult to find the optimal parameters. We have addressed this issue by fixing several bugs in the ReaxFF forces and by improving the robustness of the geometry optimization. These improvements cannot eliminate all geometry convergence issues and we recommend to avoid very flexible geometries in the training set.</div><div>Both MCFF and CMA-ES are still liable to converge to sub- or near-optimal parameters, which we detected by repeating the calibration with different random seeds. The existence of distinct near-optimal parameter vectors is a general pattern throughout our study and provides opportunities to improve the training set or to detect overfitting artifacts.</div>

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

10.26434/chemrxiv.6606668 ◽

2019 ◽

Author(s):

Ganna Shchygol ◽

Alexei Yakovlev ◽

Tomáš Trnka ◽

Adri C.T. van Duin ◽

Toon Verstraelen

Keyword(s):

Monte Carlo ◽

Force Field ◽

Parameter Optimization ◽

Optimal Parameter ◽

Global Optimum ◽

Evolutionary Strategy ◽

Computationally Efficient ◽

Training Set ◽

Covariance Matrix Adaptation ◽

Numerical Noise

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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Millimetre-wave and infrared spectroscopy of Br13 CN: anharmonic force field of cyanogen bromide from spectroscopic data and ab initio calculations

Molecular Physics ◽

10.1080/00268979709482631 ◽

1997 ◽

Vol 90 (3) ◽

pp. 495-497

Author(s):

CLAUDIO ESPOSTI ◽

FILIPPO TAMASSIA ◽

CRISTINA PUZZARINI ◽

RICCARDO TARRONI ◽

ZDENEK ZELINGER

Keyword(s):

Infrared Spectroscopy ◽

Force Field ◽

Ab Initio Calculations ◽

Ab Initio ◽

Spectroscopic Data ◽

Cyanogen Bromide ◽

Millimetre Wave ◽

Anharmonic Force

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Symmetry Based Material Optimization

Symmetry ◽

10.3390/sym13020315 ◽

2021 ◽

Vol 13 (2) ◽

pp. 315

Author(s):

Zeyun Shi ◽

Jinkeng Lin ◽

Jiong Chen ◽

Yao Jin ◽

Jin Huang

Keyword(s):

Force Field ◽

Vector Fields ◽

General Situation ◽

Kernel Space ◽

Dynamic Simulations ◽

Physical Behavior ◽

Material Parameters ◽

Material Optimization ◽

The Asymmetry ◽

Asymmetric Mesh

Many man-made or natural objects are composed of symmetric parts and possess symmetric physical behavior. Although its shape can exactly follow a symmetry in the designing or modeling stage, its discretized mesh in the analysis stage may be asymmetric because generating a mesh exactly following the symmetry is usually costly. As a consequence, the expected symmetric physical behavior may not be faithfully reproduced due to the asymmetry of the mesh. To solve this problem, we propose to optimize the material parameters of the mesh for static and kinematic symmetry behavior. Specifically, under the situation of static equilibrium, Young’s modulus is properly scaled so that a symmetric force field leads to symmetric displacement. For kinematics, the mass is optimized to reproduce symmetric acceleration under a symmetric force field. To efficiently measure the deviation from symmetry, we formulate a linear operator whose kernel contains all the symmetric vector fields, which helps to characterize the asymmetry error via a simple ℓ2 norm. To make the resulting material suitable for the general situation, the symmetric training force fields are derived from modal analysis in the above kernel space. Results show that our optimized material significantly reduces the asymmetric error on an asymmetric mesh in both static and dynamic simulations.

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Estimating a Stoichiometric Solid’s Gibbs Free Energy Model by Means of a Constrained Evolutionary Strategy

Materials ◽

10.3390/ma14020471 ◽

2021 ◽

Vol 14 (2) ◽

pp. 471

Author(s):

Constantino Grau Turuelo ◽

Sebastian Pinnau ◽

Cornelia Breitkopf

Keyword(s):

Heat Capacity ◽

Free Energy ◽

Evolutionary Strategy ◽

Data Sets ◽

Automatic Data ◽

Physical Constraints ◽

Stoichiometric Model ◽

Covariance Matrix Adaptation ◽

Dsc Measurements ◽

Capacity Data

Modeling of thermodynamic properties, like heat capacities for stoichiometric solids, includes the treatment of different sources of data which may be inconsistent and diverse. In this work, an approach based on the covariance matrix adaptation evolution strategy (CMA-ES) is proposed and described as an alternative method for data treatment and fitting with the support of data source dependent weight factors and physical constraints. This is applied to a Gibb’s Free Energy stoichiometric model for different magnesium sulfate hydrates by means of the NASA9 polynomial. Its behavior is proved by: (i) The comparison of the model to other standard methods for different heat capacity data, yielding a more plausible curve at high temperature ranges; (ii) the comparison of the fitted heat capacity values of MgSO4·7H2O against DSC measurements, resulting in a mean relative error of a 0.7% and a normalized root mean square deviation of 1.1%; and (iii) comparing the Van’t Hoff and proposed Stoichiometric model vapor-solid equilibrium curves to different literature data for MgSO4·7H2O, MgSO4·6H2O, and MgSO4·1H2O, resulting in similar equilibrium values, especially for MgSO4·7H2O and MgSO4·6H2O. The results show good agreement with the employed data and confirm this method as a viable alternative for fitting complex physically constrained data sets, while being a potential approach for automatic data fitting of substance data.

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