Thermal Effects on the Friction of Sliding Rough Surfaces

Author(s):  
Peter Spijker ◽  
Guillaume Anciaux ◽  
Jean-Franc¸ois Molinari

We use large-scale molecular dynamics (MD) simulations to model the sliding of deformable bodies in close contact, where both bodies have self-affine rough surfaces at the atomistic level. We keep both bodies at room temperature, while changing the surface roughness and the applied load. We compared our current results with similar cases at zero Kelvin and find surprisingly that a mild thermal increase from 0 to 300 K of the material enhances friction at the atomic scale.

2014 ◽  
Vol 1700 ◽  
pp. 61-66
Author(s):  
Guttormur Arnar Ingvason ◽  
Virginie Rollin

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.


Soft Matter ◽  
2018 ◽  
Vol 14 (15) ◽  
pp. 2796-2807 ◽  
Author(s):  
Andrea Catte ◽  
Mark R. Wilson ◽  
Martin Walker ◽  
Vasily S. Oganesyan

Antimicrobial action of a cationic peptide is modelled by large scale MD simulations.


Author(s):  
Juan J Galano-Frutos ◽  
Helena García-Cebollada ◽  
Javier Sancho

Abstract The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical–chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80–85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore’s law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.


MRS Advances ◽  
2017 ◽  
Vol 2 (29) ◽  
pp. 1571-1576
Author(s):  
Vinicius Splugues ◽  
Pedro Alves da Silva Autreto ◽  
Douglas S. Galvao

ABSTRACTThe advent of graphene created a revolution in materials science. Because of this there is a renewed interest in other carbon-based structures. Graphene is the ultimate (just one atom thick) membrane. It has been proposed that graphene can work as impermeable membrane to standard gases, such argon and helium. Graphene-like porous membranes, but presenting larger porosity and potential selectivity would have many technological applications. Biphenylene carbon (BPC), sometimes called graphenylene, is one of these structures. BPC is a porous two-dimensional (planar) allotrope carbon, with its pores resembling typical sieve cavities and/or some kind of zeolites. In this work, we have investigated the hydrogenation dynamics of BPC membranes under different conditions (hydrogenation plasma density, temperature, etc.). We have carried out an extensive study through fully atomistic molecular dynamics (MD) simulations using the reactive force field ReaxFF, as implemented in the well-known Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. Our results show that the BPC hydrogenation processes exhibit very complex patterns and the formation of correlated domains (hydrogenated islands) observed in the case of graphene hydrogenation was also observed here. MD results also show that under hydrogenation BPC structure undergoes a change in its topology, the pores undergoing structural transformations and extensive hydrogenation can produce significant structural damages, with the formation of large defective areas and large structural holes, leading to structural collapse.


RSC Advances ◽  
2020 ◽  
Vol 10 (16) ◽  
pp. 9187-9192 ◽  
Author(s):  
Hui Feng ◽  
Jingwen Tang ◽  
Haotian Chen ◽  
Yuanyuan Tian ◽  
Qihong Fang ◽  
...  

Using large-scale molecular dynamics (MD) simulations, the effects of interface and layer number in the nanoindentation response of experimentally observed nanotwinned Cu/high entropy alloy (HEA) FeCoCrNi nanolaminate are studied.


2015 ◽  
Vol 17 (13) ◽  
pp. 8480-8490 ◽  
Author(s):  
Volker Lesch ◽  
Andreas Heuer ◽  
Christian Holm ◽  
Jens Smiatek

We study the solvation properties of the ionic liquid 1-ethyl-3-methylimidazolium acetate ([eMIM]+[ACE]−) and the resulting dynamic behavior for differently charged model solutes at room temperature via atomistic molecular dynamics (MD) simulations of 500 ns length.


Author(s):  
Quang-Cherng Hsu ◽  
Chien-Liang Lin ◽  
Te-Hua Fang

This paper aims at the study on nanoimprint lithography (NIL) of the polymer material in (CH2)n Chains. The simulation codes were built based on molecular dynamics (MD) method for observing material deformation behaviors in atomic scale. The deformation mechanism of NIL of polymer material (CH2)n pressed by silicon stamp was first studied, by which the effects of critical punch tip width, imprint depth, temperature, and adhesion effect were studied. Next, the nanoimprint processes with stamp tips covered by anti-adhesion material, which is a self-assembled monolayer (SAM), were studied to compare to those processes without having anti-adhesion layer. When deforming polymer material at or above room temperature, adhesion problems occur between stamp and polymer. Polymer materials adhere to stamp more severe than they adhere to each others because potential energies between long chains of polymers are smaller than those between polymer and stamp. From the relation between system energy and stamp translation based on the MD simulations, the system energy increases when stamp moves gradually. When unloading, the system energy will return to its minimum energy status and remains stable. However, when punch leaves polymer materials, energy fluctuation occurs due to some polymer materials adhere to the stamp. Finally, the analysis of stamp with and without SAM based on the MD method was conducted and discussed.


2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>


2019 ◽  
Author(s):  
Junghoon Chae ◽  
Debsindhu Bhowmik ◽  
Heng Ma ◽  
Arvind Ramanathan ◽  
Chad Steed

AbstractMolecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).


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