Potential Optimization Software for Materials (POSMat)

Martinez, Jhonattan G.; Chernatynskiy, Aleksandr V.; Yılmaz, Dündar E.; Liang, Tao; Sinnott, Susan B.; Phillpot, Simon R.

doi:10.1016/j.cpc.2016.01.015

Cited by 14 publications

(8 citation statements)

References 35 publications

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“…(2) Optimizers expect a different input all together. (3) The format in which parameters are stored is specific to each method. − The combination of these oftentimes results in works that can be hardly comprehended and reproduced by third parties. In an effort to address the above issues, we introduce the ParAMS scripting package for Python.…”

Section: Introductionmentioning

confidence: 99%

ParAMS: Parameter Optimization for Atomistic and Molecular Simulations

Komissarov

Rüger

Hellström

et al. 2021

J. Chem. Inf. Model.

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This work introduces ParAMSa versatile Python package that aims to make parametrization workflows in computational chemistry and physics more accessible, transparent, and reproducible. We demonstrate how ParAMS facilitates the parameter optimization for potential energy surface (PES) models, which can otherwise be a tedious specialist task. Because of the package's modular structure, various functionality can be easily combined to implement a diversity of parameter optimization protocols. For example, the choice of PES model and the parameter optimization algorithm can be selected independently. An illustration of ParAMS' strengths is provided in two case studies: (i) a density functional-based tight binding (DFTB) repulsive potential for the inorganic ionic crystal ZnO and (ii) a ReaxFF force field for the simulation of organic disulfides.

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Section: Introductionmentioning

confidence: 99%

ParAMS: Parameter Optimization for Atomistic and Molecular Simulations

Komissarov

Rüger

Hellström

et al. 2021

J. Chem. Inf. Model.

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“…In general, the calibration of force fields is an optimization process, where the parameters of the force fields are tuned to minimize the difference between the predicted property from the MD simulation and the reference value. 12 When multiple properties need to be considered simultaneously, this process becomes the multiobjective optimization. Very limited work has been done for multiobjective force field calibration.…”

Section: ■ Introductionmentioning

confidence: 99%

“…DFT models also need to be calibrated, and the associated errors can be propagated to force fields. In general, the calibration of force fields is an optimization process, where the parameters of the force fields are tuned to minimize the difference between the predicted property from the MD simulation and the reference value . When multiple properties need to be considered simultaneously, this process becomes the multiobjective optimization.…”

Section: Introductionmentioning

confidence: 99%

Coarse-Grained Force Field Calibration Based on Multiobjective Bayesian Optimization to Simulate Water Diffusion in Poly-ε-caprolactone

et al. 2020

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Molecular dynamics at the atomistic scale is increasingly being used to predict material properties and speed up the material design and development process. However, the accuracy of molecular dynamics predictions is sensitively dependent on the force fields. In the traditional force field calibration process, a specific property, predicted by the model, is compared with the experimental observation and the force field parameters are adjusted to minimize the difference. This leads to the issue that the calibrated force fields are not generic and robust enough to predict different properties. Here, a new calibration method based on multiobjective Bayesian optimization is developed to speed up the development of molecular dynamics force fields that are capable of predicting multiple properties accurately. This is achieved by reducing the number of simulation runs to generate the Pareto front with an efficient sequential sampling strategy. The methodology is demonstrated by generating a new coarse-grained force field for polycaprolactone, where the force field can predict the mechanical properties and water diffusion in the polymer.

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“…A number of different formulations are often used and various techniques can be applied for developing these potentials, but understanding the uncertainty that enters into molecular dynamics simulations requires a firm understanding of the relationship between the interatomic potential, the responses included in the training set, and the properties computed in atomistic simulations. In fact, there is an increasing amount of interatomic potential research in the community with regards to cataloging interatomic potentials, tests, and properties [1][2][3][4], optimization techniques for high-dimensional spaces [5][6][7], and understanding the change (or uncertainty) in properties over multiple potentials [8][9][10], all of which are aimed at bringing further understanding to the material constitutive model (i.e., interatomic potentials) that drives atomistic simulations.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Parameter Sensitivity and Uncertainty for Interatomic Potential Design: Application to Saturated Hydrocarbons

Tschopp

Rinderspacher

Nouranian

et al. 2017

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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The research objective herein is to understand the relationships between the interatomic potential parameters and properties used in the training and validation of potentials, specifically using a recently developed modified embedded-atom method (MEAM) potential for saturated hydrocarbons (C–H system). This potential was parameterized to a training set that included bond distances, bond angles, and atomization energies at 0 K of a series of alkane structures from methane to n-octane. In this work, the parameters of the MEAM potential were explored through a fractional factorial design and a Latin hypercube design to better understand how individual MEAM parameters affected several properties of molecules (energy, bond distances, bond angles, and dihedral angles) and also to quantify the relationship/correlation between various molecules in terms of these properties. The generalized methodology presented shows quantitative approaches that can be used in selecting the appropriate parameters for the interatomic potential, selecting the bounds for these parameters (for constrained optimization), selecting the responses for the training set, selecting the weights for various responses in the objective function, and setting up the single/multi-objective optimization process itself. The significance of the approach applied in this study is not only the application to the C–H system but that the broader framework can also be easily applied to any number of systems to understand the significance of parameters, their relationships to properties, and the subsequent steps for designing interatomic potentials under uncertainty.

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Potential Optimization Software for Materials (POSMat)

Cited by 14 publications

References 35 publications

ParAMS: Parameter Optimization for Atomistic and Molecular Simulations

ParAMS: Parameter Optimization for Atomistic and Molecular Simulations

Coarse-Grained Force Field Calibration Based on Multiobjective Bayesian Optimization to Simulate Water Diffusion in Poly-ε-caprolactone

Quantifying Parameter Sensitivity and Uncertainty for Interatomic Potential Design: Application to Saturated Hydrocarbons

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