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.

Efficient global optimization of reactive force-field parameters

2015 ◽  
Vol 36 (20) ◽  
pp. 1550-1561 ◽  
Author(s):  
Mark Dittner ◽  
Julian Müller ◽  
Hasan Metin Aktulga ◽  
Bernd Hartke
Keyword(s):  
Global Optimization ◽  
Force Field ◽  
Efficient Global Optimization ◽  
Reactive Force ◽  
Reactive Force Field ◽  
Force Field Parameters

Intelligent-ReaxFF: Evaluating the reactive force field parameters with machine learning

2020 ◽  
Vol 172 ◽  
pp. 109393 ◽  
Author(s):  
Feng Guo ◽  
Yu-Shi Wen ◽  
Shi-Quan Feng ◽  
Xiao-Dong Li ◽  
Heng-Shuai Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Reactive Force ◽  
Reactive Force Field ◽  
Force Field Parameters

Optimization of a New Reactive Force Field for Silver - Based Materials

2020 ◽  
Author(s):  
Clément Dulong ◽  
Bruno Madebène ◽  
Susanna Monti ◽  
Johannes Richardi
Keyword(s):  
Force Field ◽  
Self Assembled Monolayers ◽  
The Novel ◽  
Reactive Force ◽  
Reactive Force Field ◽  
Energetic Properties ◽  
Extended Training ◽  
Geometrical Features ◽  
Assembled Monolayers ◽  
Gold Thiolate

<div><div><div><p>A new reactive force field based on the ReaxFF formalism is effectively parametrized against an extended training set of quantum chemistry data (containing more than 120 different structures) to describe accurately silver- and silver-thiolate systems. The results obtained with this novel representation demonstrate that the novel ReaxFF paradigm is a powerful methodology to reproduce more appropriately average geometric and energetic properties of metal clusters and slabs when compared to the earlier ReaxFF parametrizations dealing with silver and gold. ReaxFF cannot describe adequately specific geometrical features such as the observed shorter distances between the under-coordinated atoms at the cluster edges. Geometric and energetic properties of thiolates adsorbed on a silver Ag20 pyramid are correctly represented by the new ReaxFF and compared with results for gold. The simulation of self-assembled monolayers of thiolates on a silver (111) surface does not indicate the formation of staples in contrast to the results for gold-thiolate systems.</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>

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>

scholarly journals Ultra-strong bio-glue from genetically engineered polypeptides

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chao Ma ◽  
Jing Sun ◽  
Bo Li ◽  
Yang Feng ◽  
Yao Sun ◽  
...  
Keyword(s):  
Soft Tissues ◽  
Covalent Bond ◽  
Genetically Engineered ◽  
Supramolecular Interactions ◽  
Molar Ratio ◽  
High Adhesion ◽  
Strong Adhesion ◽  
Order Of Magnitude

AbstractThe development of biomedical glues is an important, yet challenging task as seemingly mutually exclusive properties need to be combined in one material, i.e. strong adhesion and adaption to remodeling processes in healing tissue. Here, we report a biocompatible and biodegradable protein-based adhesive with high adhesion strengths. The maximum strength reaches 16.5 ± 2.2 MPa on hard substrates, which is comparable to that of commercial cyanoacrylate superglue and higher than other protein-based adhesives by at least one order of magnitude. Moreover, the strong adhesion on soft tissues qualifies the adhesive as biomedical glue outperforming some commercial products. Robust mechanical properties are realized without covalent bond formation during the adhesion process. A complex consisting of cationic supercharged polypeptides and anionic aromatic surfactants with lysine to surfactant molar ratio of 1:0.9 is driven by multiple supramolecular interactions enabling such strong adhesion. We demonstrate the glue’s robust performance in vitro and in vivo for cosmetic and hemostasis applications and accelerated wound healing by comparison to surgical wound closures.

scholarly journals Development of a reactive force field for CaCl2·nH2O, and the application to thermochemical energy storage

2021 ◽  
Vol 197 ◽  
pp. 110595
Author(s):  
Koen Heijmans ◽  
Sophie Nab ◽  
Bern Klein Holkenborg ◽  
Amar Deep Pathak ◽  
Silvia Gaastra-Nedea ◽  
...  
Keyword(s):  
Energy Storage ◽  
Force Field ◽  
Reactive Force ◽  
Reactive Force Field

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
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