Conformational Study of the PCU Cage Monopeptide: A Key Role of Some Force-Field Parameters

2009 ◽  
Vol 113 (15) ◽  
pp. 5234-5238
Author(s):  
Krishna Bisetty ◽  
Juan J. Perez
Keyword(s):  
Force Field ◽  
Conformational Study ◽  
Force Field Parameters

Atomistic Modeling of Alumina/Epoxy Adhesion

2013 ◽  
Vol 1526 ◽  
Author(s):  
F.O. Valega Mackenzie ◽  
B. J. Thijsse
Keyword(s):  
Force Field ◽  
Coefficient Of Thermal Expansion ◽  
Alpha Phase ◽  
Atomistic Modeling ◽  
Reactive Force ◽  
Strong Adhesion ◽  
Order Of Magnitude ◽  
Force Field Parameters ◽  
Small Clusters

ABSTRACTIn this work we report a specialized reactive force field (ReaxFF) developed for the study of alumina/epoxy interfaces. Force field parameters were obtained by fitting the reactions of small clusters and separate components of epoxies on alumina surfaces in the alpha phase. We also introduce a procedure to obtain crosslinked epoxies based on a proximity criterion to drive reactions and induce crosslinking. Properties of the resulting polymer, like the coefficient of thermal expansion, are found to be of the same order of magnitude as in experiments. Molecular dynamics was used to calculate the adhesion between these polymers and different alumina surfaces: Al2O3-deficient, Al-terminated, O-terminated, 12% and 75% hydroxylated. Typical values for strong adhesion are about 0.70 J/m2 which compare well with previously reported works. The role of defects is also studied.

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

Specific force field parameters determination for the hybridab initioQM/MM LSCF method

2002 ◽  
Vol 23 (6) ◽  
pp. 610-624 ◽  
Author(s):  
Nicolas Ferré ◽  
Xavier Assfeld ◽  
Jean-Louis Rivail
Keyword(s):  
Force Field ◽  
Specific Force ◽  
Force Field Parameters

scholarly journals New CHARMM force field parameters for dehydrated amino acid residues, the key to lantibiotic molecular dynamics simulations

RSC Advances ◽  
2014 ◽  
Vol 4 (89) ◽  
pp. 48621-48631 ◽  
Author(s):  
Eleanor R. Turpin ◽  
Sam Mulholland ◽  
Andrew M. Teale ◽  
Boyan B. Bonev ◽  
Jonathan D. Hirst
Keyword(s):  
Molecular Dynamics ◽  
Amino Acid ◽  
Force Field ◽  
Molecular Dynamics Simulations ◽  
Amino Acid Residues ◽  
Charmm Force Field ◽  
Force Field Parameters ◽  
Dynamics Simulations

Hydration of platinum(II) complexes: a molecular mechanics study using atom-based force-field parameters

2000 ◽  
Vol 104 (3-4) ◽  
pp. 247-251 ◽  
Author(s):  
Jacqueline Langlet ◽  
Jacqueline Berg�s ◽  
Jacqueline Caillet ◽  
Jiri Kozelka
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Force Field Parameters

Tuning force field parameters of ionic liquids using machine learning techniques

2021 ◽  
Vol 200 ◽  
pp. 110759
Author(s):  
Rafikul Islam ◽  
Md Fauzul Kabir ◽  
Saugato Rahman Dhruba ◽  
Khurshida Afroz
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Force Field ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Force Field Parameters

VFFDT: A New Software for Preparing AMBER Force Field Parameters for Metal-Containing Molecular Systems

2016 ◽  
Vol 56 (4) ◽  
pp. 811-818 ◽  
Author(s):  
Suqing Zheng ◽  
Qing Tang ◽  
Jian He ◽  
Shiyu Du ◽  
Shaofang Xu ◽  
...  
Keyword(s):  
Force Field ◽  
Amber Force Field ◽  
Molecular Systems ◽  
Force Field Parameters
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