Molecular Dynamics of DNA: Comparison of Force Fields and Terminal Nucleotide Definitions

2010 ◽  
Vol 114 (30) ◽  
pp. 9882-9893 ◽  
Author(s):  
Clarisse G. Ricci ◽  
Alex S. C. de Andrade ◽  
Melina Mottin ◽  
Paulo A. Netz
Keyword(s):  
Molecular Dynamics ◽  
Force Fields ◽  
Terminal Nucleotide

scholarly journals Corrections to “Do Molecular Dynamics Force Fields Capture Conformational Dynamics of Alanine in Water?”

2020 ◽  
Vol 16 (9) ◽  
pp. 5982-5982
Author(s):  
Shuting Zhang ◽  
Reinhard Schweitzer-Stenner ◽  
Brigita Urbanc
Keyword(s):  
Molecular Dynamics ◽  
Force Fields ◽  
Conformational Dynamics

scholarly journals High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling

2021 ◽  
Author(s):  
Théo Jaffrelot Inizan ◽  
Frédéric Célerse ◽  
Olivier Adjoua ◽  
Dina El Ahdab ◽  
Luc-Henri Jolly ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
High Resolution ◽  
Adaptive Sampling ◽  
Force Fields ◽  
Sampling Strategy ◽  
Md Simulations ◽  
Conformational Space ◽  
Many Body ◽  
Polarizable Force Fields ◽  
Main Protease

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

Simulations of the Quartz(101̅1)/Water Interface: A Comparison of Classical Force Fields, Ab Initio Molecular Dynamics, and X-ray Reflectivity Experiments

2011 ◽  
Vol 115 (5) ◽  
pp. 2076-2088 ◽  
Author(s):  
A. A. Skelton ◽  
P. Fenter ◽  
J. D. Kubicki ◽  
D. J. Wesolowski ◽  
P. T. Cummings
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Force Fields ◽  
Ab Initio Molecular Dynamics ◽  
Water Interface ◽  
X Ray

Experiment vs Force Fields:  DNA Conformation from Molecular Dynamics Simulations

1997 ◽  
Vol 101 (38) ◽  
pp. 7361-7363 ◽  
Author(s):  
Michael Feig ◽  
B. Montgomery Pettitt
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Force Fields ◽  
Dna Conformation ◽  
Dynamics Simulations

scholarly journals A universal framework for featurization of atomistic systems

2021 ◽  
Author(s):  
Xiangyun Lei ◽  
Andrew Medford
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Force Fields ◽  
Reactive Systems ◽  
Quantum Molecular Dynamics ◽  
Learning Models ◽  
Fixed Dimension ◽  
Comparable Performance ◽  
Invaluable Tool ◽  
Dynamics Simulations

Abstract Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields.

scholarly journals Self-Diffusion Coefficients of Methane/n-Hexane Mixtures at High Pressures: An Evaluation of the Finite-Size Effect and a Comparison of Force Fields

2019 ◽  
Author(s):  
Thiago José Pinheiro dos Santos ◽  
Charlles Abreu ◽  
Bruno Horta ◽  
Frederico W. Tavares
Keyword(s):  
Molecular Dynamics ◽  
Diffusion Coefficients ◽  
High Pressures ◽  
Force Fields ◽  
Transport Coefficients ◽  
Finite Size ◽  
Finite Size Effect ◽  
Self Diffusion ◽  
Analytical Correction ◽  
Dynamics Simulations

Mass transport coefficients play an important role in process design and in compositional grading of oil reservoirs. As experimental measurements of these properties can be costly and hazardous, Molecular Dynamics simulations emerge as an alternative approach. In this work, we used Molecular Dynamics to calculate the self-diffusion coefficients of methane/n-hexane mixtures at different conditions, in both liquid and supercritical phases. We evaluated how the finite box size and the choice of the force field affect the calculated properties at high pressures. Results show a strong dependency between self-diffusion and the simulation box size. The Yeh-Hummer analytical correction [J. Phys. Chem. B, 108, 15873 (2004)] can attenuate this effect, but sometimes makes the results depart from experimental data due to issues concerning the force fields. We have also found that different all-atom and united-atom models can produce biased results due to caging effects and to different dihedral configurations of the n-alkane.

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