scholarly journals A Novel Approach to Atomistic Molecular Dynamics Simulation of Phenolic Resins Using Symthons

Polymers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 926
Author(s):  
Matthew A. Bone ◽  
Terence Macquart ◽  
Ian Hamerton ◽  
Brendan J. Howlin
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Computational Simulation ◽  
Dynamics Simulation ◽  
Phenolic Resins ◽  
Novel Approach ◽  
Starting Point ◽  
Initial Monomer ◽  
Computational Materials

Materials science is beginning to adopt computational simulation to eliminate laboratory trial and error campaigns—much like the pharmaceutical industry of 40 years ago. To further computational materials discovery, new methodology must be developed that enables rapid and accurate testing on accessible computational hardware. To this end, the authors utilise a novel methodology concept of intermediate molecules as a starting point, for which they propose the term ‘symthon’ (The term ‘Symthon’ is being used as a simulation equivalent of the synthon, popularised by Dr Stuart Warren in ‘Organic Synthesis: The Disconnection Approach’, OUP: Oxford, 1983.) rather than conventional monomers. The use of symthons eliminates the initial monomer bonding phase, reducing the number of iterations required in the simulation, thereby reducing the runtime. A novel approach to molecular dynamics, with an NVT (Canonical) ensemble and variable unit cell geometry, was used to generate structures with differing physical and thermal properties. Additional script methods were designed and tested, which enabled a high degree of cure in all sampled structures. This simulation has been trialled on large-scale atomistic models of phenolic resins, based on a range of stoichiometric ratios of formaldehyde and phenol. Density and glass transition temperature values were produced, and found to be in good agreement with empirical data and other simulated values in the literature. The runtime of the simulation was a key consideration in script design; cured models can be produced in under 24 h on modest hardware. The use of symthons has been shown as a viable methodology to reduce simulation runtime whilst generating accurate models.

Large-Scale Molecular Dynamics Simulations of Interstitial Defect Diffusion in Silicon

2002 ◽  
Vol 731 ◽  
Author(s):  
David A. Richie ◽  
Jeongnim Kim ◽  
Richard Hennig ◽  
Kaden Hazzard ◽  
Steve Barr ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Tight Binding ◽  
Md Simulations ◽  
Binding Potential ◽  
Interstitial Defect ◽  
Long Time ◽  
Computational Materials ◽  
Dynamics Simulations

AbstractThe simulation of defect dynamics and evolution is a technologicaly relevant challenge for computational materials science. The diffusion of small defects in silicon unfolds as a sequence of structural transitions. The relative infrequency of transition events requires simulation over extremely long time scales. We simulate the diffusion of small interstitial clusters (I1, I2, I3) for a range of temperatures using large-scale molecular dynamics (MD) simulations with a realistic tight-binding potential. A total of 0.25 μ sec of simulation time is accumulated for the study. A novel real-time multiresolution analysis (RTMRA) technique extracts stable structures directly from the dynamics without structural relaxation. The discovered structures are relaxed to confirm their stability.

Large-scale molecular dynamics simulation of crosslinked phenolic resins using pseudo-reaction model

Polymer ◽  
2016 ◽  
Vol 103 ◽  
pp. 261-276 ◽  
Author(s):  
Yasuyuki Shudo ◽  
Atsushi Izumi ◽  
Katsumi Hagita ◽  
Toshio Nakao ◽  
Mitsuhiro Shibayama
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Large Scale ◽  
Reaction Model ◽  
Dynamics Simulation ◽  
Phenolic Resins

scholarly journals Molecular Dynamics Simulation of Iron — A Review

SPIN ◽  
2015 ◽  
Vol 05 (04) ◽  
pp. 1540007 ◽  
Author(s):  
C. P. Chui ◽  
Wenqing Liu ◽  
Yongbing Xu ◽  
Yan Zhou
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Review Paper ◽  
Scientific Discovery ◽  
Pure Metal ◽  
Dynamics Simulation ◽  
Interatomic Potentials ◽  
Metal Form ◽  
Metal Properties

Molecular dynamics (MD) is a technique of atomistic simulation which has facilitated scientific discovery of interactions among particles since its advent in the late 1950s. Its merit lies in incorporating statistical mechanics to allow for examination of varying atomic configurations at finite temperatures. Its contributions to materials science from modeling pure metal properties to designing nanowires is also remarkable. This review paper focuses on the progress of MD in understanding the behavior of iron — in pure metal form, in alloys, and in composite nanomaterials. It also discusses the interatomic potentials and the integration algorithms used for simulating iron in the literature. Furthermore, it reveals the current progress of MD in simulating iron by exhibiting some results in the literature. Finally, the review paper briefly mentions the development of the hardware and software tools for such large-scale computations.

Combustion Driven by Fragment-based Ab Initio Molecular Dynamics Simulation

2019 ◽  
Author(s):  
Liqun Cao ◽  
Jinzhe Zeng ◽  
Mingyuan Xu ◽  
Chih-Hao Chin ◽  
Tong Zhu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Large Scale ◽  
Methane Combustion ◽  
Combustion Method ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Computational Time ◽  
Combustion Mechanism ◽  
Daily Lives

Combustion is a kind of important reaction that affects people's daily lives and the development of aerospace. Exploring the reaction mechanism contributes to the understanding of combustion and the more efficient use of fuels. Ab initio quantum mechanical (QM) calculation is precise but limited by its computational time for large-scale systems. In order to carry out reactive molecular dynamics (MD) simulation for combustion accurately and quickly, we develop the MFCC-combustion method in this study, which calculates the interaction between atoms using QM method at the level of MN15/6-31G(d). Each molecule in systems is treated as a fragment, and when the distance between any two atoms in different molecules is greater than 3.5 Å, a new fragment involved two molecules is produced in order to consider the two-body interaction. The deviations of MFCC-combustion from full system calculations are within a few kcal/mol, and the result clearly shows that the calculated energies of the different systems using MFCC-combustion are close to converging after the distance thresholds are larger than 3.5 Å for the two-body QM interactions. The methane combustion was studied with the MFCC-combustion method to explore the combustion mechanism of the methane-oxygen system.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

Sputtering of Graphite by Hydrogen Isotopes in the Fusion Environment: A Molecular Dynamics Simulation Study

2018 ◽  
Vol 4 (4) ◽  
Author(s):  
Qiang Zhao ◽  
Yang Li ◽  
Zheng Zhang ◽  
Xiaoping Ouyang
Keyword(s):  
Molecular Dynamics ◽  
Incident Energy ◽  
Large Scale ◽  
Incident Angle ◽  
Hydrogen Isotopes ◽  
Dynamics Simulation ◽  
Calculation Results ◽  
Sputtering Yield ◽  
Increasing Temperature ◽  
Sputtering Yields

The sputtering of graphite due to the bombardment of hydrogen isotopes is crucial to successfully using graphite in the fusion environment. In this work, we use molecular dynamics to simulate the sputtering using the large-scale atomic/molecular massively parallel simulator (lammps). The calculation results show that the peak values of the sputtering yield are between 25 eV and 50 eV. When the incident energy is greater than the energy corresponding to the peak value, a lower carbon sputtering yield is obtained. The temperature that is most likely to sputter is approximately 800 K for hydrogen, deuterium, and tritium. Below the 800 K, the sputtering yields increase with temperature. By contrast, above the 800 K, the yields decrease with increasing temperature. Under the same temperature and incident energy, the sputtering rate of tritium is greater than that of deuterium, which in turn is greater than that of hydrogen. When the incident energy is 25 eV, the sputtering yield at 300 K increases below an incident angle at 30 deg and remains steady after that.

Molecular Dynamics Simulation of a Pullout Test on a Carbon Nanotube in a Polymer Matrix

2014 ◽  
Vol 1700 ◽  
pp. 61-66
Author(s):  
Guttormur Arnar Ingvason ◽  
Virginie Rollin
Keyword(s):  
Molecular Dynamics ◽  
Polymer Matrix ◽  
Large Scale ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Atomistic Simulations ◽  
Polymer Molecules ◽  
Molecular Potential ◽  
Pull Out ◽  
Walled Carbon Nanotubes

ABSTRACTAdding single walled carbon nanotubes (SWCNT) to a polymer matrix can improve the delamination properties of the composite. Due to the complexity of polymer molecules and the curing process, few 3-D Molecular Dynamics (MD) simulations of a polymer-SWCNT composite have been run. Our model runs on the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), with a COMPASS (Condensed phase Optimized Molecular Potential for Atomistic Simulations Studies) potential. This potential includes non-bonded interactions, as well as bonds, angles and dihedrals to create a MD model for a SWCNT and EPON 862/DETDA (Diethyltoluenediamine) polymer matrix. Two simulations were performed in order to test the implementation of the COMPASS parameters. The first one was a tensile test on a SWCNT, leading to a Young’s modulus of 1.4 TPa at 300K. The second one was a pull-out test of a SWCNT from an originally uncured EPON 862/DETDA matrix.

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)

2021 ◽  
Author(s):  
Kai Xu ◽  
Lei Yan ◽  
Bingran You
Keyword(s):  
Molecular Dynamics ◽  
Active Learning ◽  
Force Field ◽  
Density Functional ◽  
Process Model ◽  
Large Scale ◽  
Computational Cost ◽  
Dynamics Simulation ◽  
Potential Energy Landscape ◽  
Three Body

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.

scholarly journals Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein–Peptide Docking and Molecular Dynamics Simulation

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 3018 ◽  
Author(s):  
Gao Tu ◽  
Tingting Fu ◽  
Fengyuan Yang ◽  
Lixia Yao ◽  
Weiwei Xue ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Electrostatic Interactions ◽  
Binding Free Energy ◽  
Computational Simulation ◽  
Critical Role ◽  
Dynamics Simulation ◽  
Peptide Docking ◽  
C Terminus ◽  
Free Energy Decomposition

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290–1310 of GluN2B (GluN2B-CT1290-1310) remain elusive and the interaction between GluN2B-CT1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT1290-1310 was predicted by computational simulation methods including protein–peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT1290–1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein–peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT1290-1310 and DAPK1 interface.

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