Employing SAFT Coarse-Grained Force Fields for the Molecular Simulation of Thermodynamic and Transport Properties of CO2–n-Alkane Mixtures

2019 ◽  
Vol 65 (3) ◽  
pp. 1159-1171
Author(s):  
Lingru Zheng ◽  
Fernando Bresme ◽  
J. P. Martin Trusler ◽  
Erich A. Müller
Keyword(s):  
Transport Properties ◽  
Molecular Simulation ◽  
Force Fields ◽  
Coarse Grained ◽  
Thermodynamic And Transport Properties

An Estimation of Thermodynamic and Transport Properties of Cryogenic Hydrogen Using Classical Molecular Simulation

2011 ◽  
Author(s):  
Hiroki Nagashima ◽  
Takashi Tokumasu ◽  
Shin-ichi Tsuda ◽  
Nobuyuki Tsuboi ◽  
Mitsuo Koshi ◽  
...  
Keyword(s):  
Ab Initio ◽  
Transport Properties ◽  
Molecular Simulation ◽  
Potential Model ◽  
Classical Method ◽  
Quantum Effect ◽  
Corresponding States ◽  
Density Region ◽  
Lennard Jones ◽  
Thermodynamic And Transport Properties

In this paper, we estimated the thermodynamic and transport properties of cryogenic hydrogen using classical molecular simulation to clarify the limit of classical method on the estimation of those properties of cryogenic hydrogen. Three empirical potentials, the Lennard-Jones (LJ) potential, two-center Lennard-Jones (2CLJ) potential, and modified Buckingham (exp-6) potential, and an ab initio potential model derived by the molecular orbital (MO) calculation were applied. Molecular dynamics (MD) simulations were performed across a wide density-temperature range. Using these data, the equation of state (EOS) was obtained by Kataoka’s method, and these were compared with NIST (National Institute of Standards and Technology) data according to the principle of corresponding states. Moreover, we investigated transport coefficients (viscosity coefficient, diffusion coefficient and thermal conductivity) using time correlation function. As a result, it was confirmed that the potential model has a large effect on the estimated thermodynamic and transport properties of cryogenic hydrogen. On the other hand, from the viewpoint of the principle of corresponding states, we obtained the same results from the empirical potential models as from the ab initio potential, showing that the potential model has only a small effect on the reduced EOS: the classical MD results could not reproduce the NIST data in the high-density region. This difference is thought to arise from the quantum effect in actual liquid hydrogen.

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.

Molecular simulation of structure, thermodynamic and transport properties of polymeric membrane materials for hydrocarbon separation

2005 ◽  
Vol 228-229 ◽  
pp. 15-20 ◽  
Author(s):  
Ioannis G. Economou ◽  
Vasilios E. Raptis ◽  
Vasilios S. Melissas ◽  
Doros N. Theodorou ◽  
John Petrou ◽  
...  
Keyword(s):  
Transport Properties ◽  
Molecular Simulation ◽  
Polymeric Membrane ◽  
Membrane Materials ◽  
Thermodynamic And Transport Properties

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256990
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

Finding 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 and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

Molecular simulation of thermodynamic and transport properties for the H2O+NaCl system

2014 ◽  
Vol 141 (23) ◽  
pp. 234507 ◽  
Author(s):  
Gustavo A. Orozco ◽  
Othonas A. Moultos ◽  
Hao Jiang ◽  
Ioannis G. Economou ◽  
Athanassios Z. Panagiotopoulos
Keyword(s):  
Transport Properties ◽  
Molecular Simulation ◽  
Thermodynamic And Transport Properties
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