- Accurate Force Fields for Molecular Simulation

2016 ◽  
pp. 144-159
Keyword(s):  
Molecular Simulation ◽  
Force Fields

scholarly journals Predicting hydration Gibbs energies of alkyl-aromatics using molecular simulation: a comparison of current force fields and the development of a new parameter set for accurate solvation data

2011 ◽  
Vol 13 (38) ◽  
pp. 17384 ◽  
Author(s):  
Nuno M. Garrido ◽  
Miguel Jorge ◽  
António J. Queimada ◽  
José R. B. Gomes ◽  
Ioannis G. Economou ◽  
...  
Keyword(s):  
Molecular Simulation ◽  
Force Fields ◽  
Gibbs Energies

Molecular simulation of osmometry in aqueous solutions of the BMIMCl ionic liquid: a potential route to force field parameterization of liquid mixtures

2020 ◽  
Vol 22 (48) ◽  
pp. 28325-28338
Author(s):  
Debdas Dhabal ◽  
Tanmoy Patra
Keyword(s):  
Experimental Data ◽  
Aqueous Solution ◽  
Ionic Liquid ◽  
Aqueous Solutions ◽  
Force Field ◽  
Molecular Simulation ◽  
Osmotic Coefficient ◽  
Force Fields ◽  
Liquid Mixtures ◽  
Force Field Parameterization

By means of molecular simulation, the osmotic coefficient of aqueous solution of BMIMCl ionic liquid is calculated to compare with the experimental data and use that to optimize two popular force fields available in the literature for bulk ILs.

scholarly journals Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

2021 ◽  
Author(s):  
Joe G Greener ◽  
David T Jones
Keyword(s):  
Force Field ◽  
Molecular Simulation ◽  
Protein Design ◽  
Automatic Differentiation ◽  
Force Fields ◽  
Coarse Grained ◽  
Multiple Parameters ◽  
Small Proteins ◽  
Chemical Knowledge ◽  
And Training

AbstractFinding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

scholarly journals Adsorption of CO2, N2, CH4, and their mixtures on silicalite: A critical evaluation of force fields

2020 ◽  
Vol 26 (3) ◽  
pp. 295-308
Author(s):  
Sarah Arvelos ◽  
Thalles Diógenes ◽  
Eponina Hori ◽  
Romanielo Lobato
Keyword(s):  
Force Field ◽  
Molecular Simulation ◽  
Force Fields ◽  
Critical Evaluation ◽  
Computational Power ◽  
Potential Accuracy ◽  
Simulation Results ◽  
Adsorption Of Co2 ◽  
Reliable Software ◽  
Potential Use

The use of molecular simulation has been growing in the field of engineering, fueled not just by the advances in computational power but also on the availability of reliable software. One potential use of molecular simulation is related to the screening of materials for a specific application. The reliability of molecular simulation results depends on the trustworthiness of the force field used, which for engineering purposes should be as simple as possible. This work provides an evaluation of the potential accuracy cost of using simple generic force fields to predict the adsorption of CO2, CH4, N2 and their mixtures on MFI. We employed the GCMC technique for this investigation. Different models and force fields to describe adsorbates and adsorbent were tested. The force fields performances were estimated through comparison with available adsorption experimental data. Transferability was evaluated on the prediction of pure and mixtures adsorption on CHA, LTA and FER. The results showed that a simple force field presented similar performance when compared to a more sophisticated one.

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