Molecular Dynamics Simulation of Nanocomposites using BIOVIA Materials Studio, Lammps and Gromacs

2nd Edition - April 10, 2025
Imprint: Elsevier
Author: Sumit Sharma
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 6 7 0 4 - 8
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 6 7 0 5 - 5

Molecular Dynamics Simulation of Nanocomposites using BIOVIA Materials Studio, Lammps and Gromacs, Second Edition introduces the three major software packages essential for the mo… Read more

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Molecular Dynamics Simulation of Nanocomposites using BIOVIA Materials Studio, Lammps and Gromacs, Second Edition introduces the three major software packages essential for the molecular dynamics simulation of nanocomposites, providing detailed instructions on utilizing each. This content is accompanied by real-world examples that illustrate when each should be applied. Numerous case studies demonstrate how each software package predicts various properties of nanocomposites, encompassing metal-matrix, polymer-matrix, and ceramic-matrix based nanocomposites. Explored properties include mechanical, thermal, optical, and electrical characteristics. This is a valuable resource for students, researchers, and scientists working in the field of molecular dynamics simulation.

All chapters have been fully updated to reflect the latest developments in the field, and this new edition has been enriched with additional chapters covering Al composites, machine learning, polymer coatings, and graphene-based materials and carbon nanotubes.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
Preface
Chapter 1. Introduction to molecular dynamics
1.1 Molecular dynamics
1.2 Monte Carlo simulation
1.3 Brownian dynamics
1.4 Dissipative particle dynamics
1.5 Lattice Boltzmann method
1.6 Basic concepts
1.6.1 Force field
1.6.2 Potentials
1.6.2.1 Tersoff model
1.6.2.2 Brenner model
1.6.2.3 Morse potential
1.6.2.4 Lennard-Jones potential
1.6.3 Ensemble
1.6.4 Thermostat
1.6.4.1 Andersen's method
1.6.4.2 Berendsen thermostat
1.6.4.3 Nosé–Hoover thermostat
1.6.5 Boundary conditions
1.6.5.1 Periodic boundary condition
1.6.5.2 Lees-Edwards boundary condition
1.7 Molecular dynamics methodology
1.7.1 Initial positions
1.7.1.1 Spherical systems
1.7.1.2 Nonspherical systems
1.7.2 Initial velocities
1.7.2.1 Spherical systems
1.7.2.2 Nonspherical systems
1.8 Molecular potential energy surface
Chapter 2. Overview of BIOVIA Materials Studio, LAMMPS, and GROMACS
Chapter 2.1 Overview of BIOVIA Materials Studio
2.1.1 Modules
2.1.1.1 Materials Visualizer
2.1.1.2 Adsorption locator
2.1.1.3 Amorphous cell
2.1.1.4 COMPASS
2.1.1.5 COMPASS II
2.1.1.6 Forcite
2.1.2 Simulation strategy
2.1.3 Case studies
Chapter 2.2 Overview of LAMMPS
2.2.1 Introduction to LAMMPS
2.2.2 Anatomy of a nanomechanical system
2.2.3 Internal working of LAMMPS calculations
2.2.4 Methodology of MD simulation using LAMMPS
2.2.5 Development of the unit cell model of polymeric nanocomposite
2.2.6 Setting the conditions of simulation
2.2.7 Structural properties
2.2.8 Stress–strain behavior
2.2.9 Stress relaxation
2.2.10 LAMMPS input file
2.2.11 LAMMPS output file
Chapter 2.3 Overview of GROMACS
2.3.1 Introduction
2.3.2 Working principle of GROMACS
2.3.3 Computational chemistry and molecular modeling
2.3.3.1 Molecular dynamics simulations
2.3.3.2 Molecular dynamics approximation
2.3.3.3 Energy minimization
2.3.4 Algorithms
2.3.4.1 Periodic boundary conditions
2.3.4.2 The group concept
2.3.5 Molecular dynamics
2.3.5.1 Initial conditions
2.3.5.2 Neighbor searching
2.3.5.3 Pair lists generation
2.3.5.4 Cutoff schemes: Group versus Verlet
2.3.5.5 Energy drift and pair-list buffering
2.3.5.6 Cutoff artifacts and switched interactions
2.3.5.7 Grid search
2.3.5.8 Charge groups
2.3.6 Compute forces
2.3.6.1 Potential energy
2.3.6.2 Kinetic energy and temperature
2.3.6.3 Pressure and virial
2.3.7 The leapfrog integrator
2.3.8 The velocity Verlet integrator
2.3.9 Reversible integrators: The Trotter decomposition
2.3.10 Temperature coupling
2.3.10.1 Berendsen temperature coupling
2.3.10.2 Velocity-rescaling temperature coupling
2.3.10.3 Andersen thermostat
2.3.10.4 Nose–Hoover temperature coupling
2.3.10.5 Group temperature coupling
2.3.11 Pressure coupling
2.3.11.1 Berendsen pressure coupling
2.3.11.2 Parrinello–Rahman pressure coupling
2.3.11.3 Surface-tension coupling
2.3.12 The complete update algorithm
2.3.13 Output step
2.3.14 Advantage and functional characteristics
2.3.15 Application of GROMACS
2.3.15.1 Biochip devices
2.3.15.2 Molecular modeling of biomolecules
Chapter 3. Molecular dynamics simulation of metal matrix composites using BIOVIA Materials Studio, LAMMPS, and GROMACS
Chapter 3.1 Prediction of mechanical properties of graphene/silicon carbide–reinforced aluminum composites using BIOVIA Materials Studio
3.1.1 MD methodology
3.1.2 Results and discussion
3.1.2.1 Effect of SiC volume fraction
3.1.2.2 Effect of particle size
3.1.2.3 Effect of graphene reinforcement
3.1.3 Conclusion
Chapter 3.2 Prediction of mechanical properties of graphene/copper nanolayered composites using LAMMPS
3.2.1 MD simulation
3.2.1.1 Interatomic potential
3.2.1.2 MD simulation
3.2.2 Results and discussion
3.2.2.1 Stress–strain plots
3.2.2.2 Elastic modulus and Poisson's ratio
3.2.2.3 Mechanism of deformation
3.2.3 Conclusion
Chapter 3.3 Molecular dynamics simulation of lithium metal/polymer electrolyte interfacial properties using GROMACS
3.3.1 MD simulation
3.3.2 Results and discussion
3.3.2.1 Structural properties
3.3.2.2 Dynamics of diffusion
3.3.3 Conclusions
Chapter 4. Molecular dynamics simulation of polymer–matrix composites using BIOVIA Materials Studio, LAMMPS, and GROMACS
Chapter 4.1 Molecular dynamics simulation of carbon nanotubes and polymer/carbon nanotube composites
4.1.1 Introduction
4.1.2 Layout
4.1.3 Total potential energies and interatomic forces
4.1.4 Stiffness of SWCNTS
4.1.4.1 Modeling of SWCNTs
4.1.4.2 Geometry optimization
4.1.4.3 Dynamics
4.1.4.4 Mechanical properties
4.1.5 Damping of SWCNTs
4.1.6 Thermal conductivity of SWCNTs
4.1.7 Results and discussion
4.1.7.1 Elastic moduli
4.1.7.2 Damping in SWCNTs
4.1.7.3 Thermal conductivity of SWCNTs
4.1.8 MD simulation of polymer/CNT composites
4.1.8.1 Molecular model of polymer matrix
4.1.8.2 Elastic moduli of polymers
4.1.8.3 PmPV/CNT composite system
4.1.8.4 PMMA/CNT composite system
4.1.8.5 Damping in polymer composites
4.1.8.6 Thermal conductivity
4.1.9 Conclusions
Chapter 4.2 Molecular dynamics simulation of functionalized SWCNT/polymer composites using LAMMPS
4.2.1 Introduction
4.2.2 Molecular dynamics simulation
4.2.2.1 Molecular structures
4.2.2.2 Geometry optimization
4.2.2.3 Dynamics
4.2.2.4 Mechanical properties
4.2.2.5 SWCNT/PP composites
4.2.3 Results and discussion
4.2.4 Conclusion
Chapter 4.3 Prediction of tribological properties of carbon nanotube–reinforced natural rubber composites using GROMACS
4.3.1 Introduction
4.3.2 Materials and methods
4.3.3 Results and discussion
4.3.3.1 Shear modulus
4.3.3.2 Tribological properties
4.3.3.3 Cohesive energy
4.3.3.4 Friction stresses
4.3.4 Conclusion
Chapter 5. Molecular dynamics simulation of ceramic matrix composites using BIOVIA Materials Studio, LAMMPS, and GROMACS
Chapter 5.1 Molecular dynamics simulation of carbon nanotube–reinforced silicon carbide composites using BIOVIA materials studio
5.1.1 Introduction
5.1.2 MD methodology
5.1.2.1 Geometry optimization
5.1.2.2 Dynamics
5.1.2.3 Mechanical properties
5.1.3 Results and discussion
5.1.4 Conclusion
Chapter 5.2 Molecular dynamics simulation of Al/Al2O3 metal–ceramic composite using LAMMPS
5.2.1 MD simulation
5.2.1.1 Interatomic potential
5.2.1.2 Al and Al2O3 models
5.2.2 Results and discussion
5.2.3 Conclusion
Chapter 5.3 Molecular dynamics simulation of coaxial boron nitride/carbon nanotubes using GROMACS
5.3.1 MD simulation
5.3.1.1 Interatomic potential
5.3.1.2 CNT-BNNT composite
5.3.2 Results and discussion
5.3.3 Conclusion
Chapter 6. Scripting in molecular dynamics
6.1 Working with scripts in Materials Visualizer
6.1.1 Writing scripts
6.1.2 Generating scripts
6.1.3 Checking script syntax
6.1.4 Debugging scripts
6.2 Running scripts on server
6.3 Sample scripts
6.3.1 Stress–strain script
6.3.2 Script for thermal conductivity
6.3.3 Script for glass-transition temperature
6.4 Scripting in LAMMPS
6.4.1 Script for vacancy formation energy
6.4.2 Script for deformation of a nanowire
6.5 Scripting in GROMACS
Chapter 7. Applications of BIOVIA Materials Studio, LAMMPS, and GROMACS in various fields of science and engineering
7.1 Applications of BIOVIA Materials Studio
7.1.1 Quantum tools
7.1.2 Classical simulation tools
7.1.3 Mesoscale simulation tools
7.1.4 Statistical tools
7.1.5 Analytical and crystallization tools
7.2 Applications of large-scale atomic/molecular massively parallel simulator
7.3 Applications of GROMACS
Chapter 8. Molecular dynamics modeling of 2D materials
8.1 Different types of allotropes
8.1.1 Graphyne
8.1.2 Penta-graphene
8.1.3 R–graphyne
8.1.4 Porous graphene
8.1.5 Twin graphene
8.1.6 Ψ–graphene
8.1.7 Graphenylene
8.1.8 Phagraphene
8.1.9 BC3 (boron carbide 2D sheet)
8.1.10 C3N (2D polyaniline)
8.1.11 Hybrid C3N-BC3, N doped BC3, and B doped C3N
8.2 Atomistic modeling of penta-graphene
8.2.1 Modeling
8.2.1.1 Creation of pristine graphene sheet to study volume fraction
8.2.1.2 Creation of particle data visualization file
8.2.1.3 Create data file for LAMMPS
8.2.1.4 Import LMP application from LAMMPS
8.2.1.5 Potential file (A force field parameter file)
8.2.1.6 In file
8.2.2 Molecular dynamics simulations
8.2.3 Results and discussion
8.2.3.1 Effect of temperature
8.2.3.2 Effect of strain rate
8.2.3.3 Impact of varying length and width ratio of penta-graphene sheets
8.3 Atomistic modeling of phagraphene
8.3.1 Modeling
8.3.2 Results and discussion
8.3.3 Conclusion
8.4 Atomistic modeling of C3N and N-doped boron carbide nanosheets
8.4.1 Molecular dynamics methodology
8.4.2 Results and discussion
8.4.2.1 Doping project
8.4.2.2 Vacancy defects' project
8.4.2.3 Data files creation
8.4.3 Conclusion
Chapter 9. Vibrational behavior of carbon nanotubes and graphene
9.1 Introduction
9.2 Computational methods
9.2.1 MD modeling
9.3 Results and discussion
9.3.1 Vibrational behavior of CNTs
9.3.2 Effect of aspect ratio
9.3.3 Effect of chirality
9.3.4 Effect of defects
9.3.5 Vibrational behavior of graphene
9.3.5.1 Effect of size
9.3.5.2 Effect of boundary conditions
9.3.5.3 Effect of defects
9.4 Conclusions and future scope
Chapter 10. Wear of CNT-reinforced Al composites
10.1 Introduction
10.2 Methodology
10.2.1 MD modeling of the constituents of wear test
10.2.2 Modeling of the three-layer system for the wear test
10.2.3 MD simulation for the tribological property of CNT-Al composite
10.2.4 Estimation of the tribological property of CNT-Al composite
10.3 Results and discussion
10.4 Conclusion and future aspects
Chapter 11. Machine learning and molecular dynamics
11.1 Models for predicting mechanical properties
11.1.1 Linear regression
11.1.2 Regression trees
11.1.3 Gaussian process regression
11.1.4 Stochastic gradient descent
11.1.5 Support vector machine
11.1.6 AdaBoost regression
11.1.7 Decision tree regression
11.1.8 K-nearest neighbors
11.1.9 Artificial neural network
11.2 Mechanical properties by machine learning approach
11.2.1 Research gap and future scope
Chapter 12. Polymer coatings of metallic substrate
12.1 Atomistic study of adhesion of polyurethane/polytetrafluorethylene coating on aluminum oxide surface
12.1.1 Materials and method
12.1.2 Results and discussion
12.1.3 Conclusions and future scope
12.2 Dynamic diffusion of water inside graphene-reinforced polyurethane/polytetrafluorethylene coatings: A molecular dynamics approach
12.2.1 Methodology
12.2.2 Data analysis
12.2.3 Results and discussion
12.2.3.1 Effect of temperature on the diffusion
12.2.3.2 Comparative study
12.3 Conclusions
Chapter 13. Modeling of graphene oxide and its composites
13.1 Introduction
13.2 Modeling and construction
13.2.1 Construction of natural rubber
13.2.2 Construction of graphene oxide sheet
13.2.3 Geometry optimization
13.2.4 Dynamics
13.2.5 Mechanical properties
13.2.6 Stress–strain relationships and graphs
13.2.7 Creep
13.3 Results and discussions
13.3.1 Elastic constants
13.3.1.1 Young's modulus
13.3.1.2 Bulk modulus and shear modulus
13.3.2 Experimental results involving mechanical properties obtained from literature
13.3.3 Stress–strain behavior
13.3.4 Creep characteristics
13.4 Conclusion
Chapter 14. Effect of functionalization and defects in CNT on mechanical properties and creep behavior of nitrile–butadiene rubber composites
14.1 Introduction
14.2 Modeling and methodology
14.3 Results and discussion
14.3.1 Elastic constants
14.3.2 Stress-strain behavior
14.3.3 Creep behavior
14.4 Conclusion
Chapter 15. The effect of chirality and defects on mechanical properties of carbon nanotube–reinforced polycarbonate composites
15.1 Introduction
15.1.1 Effect of defects on CNTs
15.1.2 Effect of defects on CNT–polymer composites
15.2 Methods and simulation procedure
15.2.1 Methodology
15.2.2 Modeling of PC/SWCNT composite
15.2.3 Preparation of defected PC/SWCNT composites
15.2.3.1 Creating vacancy defect in SWCNT
15.2.3.2 Creating SW defect in SWCNT
15.3 Results and discussion
15.3.1 Young's modulus of pure PC
15.3.2 Effect of chirality
15.3.3 Effect of defects
15.3.3.1 Effect of defects on (5,5) armchair SWCNT- PC composites
15.3.3.2 Effect of defects on (9,0) zigzag SWCNT-PC composites
15.3.3.3 Effect of defects on (6,4) chiral SWCNT/PC composites
15.4 Conclusion
Chapter 16. Structure of nanomaterials
16.1 Carbon nanotube and its structure
16.2 Vapor-grown carbon nanofibers
16.3 Silver nanowires
16.4 Copper nanowires
16.5 Nanorods
16.6 Quantum dots
Chapter 17. Machine learning with MD
17.1 Introduction
17.2 Calculating intrinsic properties using different approaches
17.2.1 Molecular dynamics simulation approach
17.2.2 Machine learning approach
17.3 Results and sections
17.3.1 Mechanical properties by molecular dynamics simulation
17.3.1.1 Mechanical properties of pristine as well as defected graphene
17.3.1.2 Mechanical properties of pristine BC3NS
17.3.1.3 Mechanical properties of pristine C3N
17.3.1.4 Mechanical characteristics of BC3–C3N (hybrid)
17.3.1.5 Mechanical properties of polycrystalline BC3 or C3N
17.3.1.6 Effects on mechanical properties due to defects
17.3.1.7 Effects of temperature on mechanical properties
17.3.2 Mechanical properties by machine learning approach
17.4 Research gap and future scope
Chapter 18. Applications of MD in different fields
18.1 Molecular dynamics simulation of the full satellite tobacco mosaic virus
18.2 Folding simulations of the Villin Headpiece in all-atom detail
18.3 Long continuous-trajectory simulations using Anton
18.4 Molecular dynamics for studying pyrolysis process
18.5 Molecular dynamics for studying adsorption damage
18.6 Molecular dynamics in tribological studies
Index

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Molecular Dynamics Simulation of Nanocomposites using BIOVIA Materials Studio, Lammps and Gromacs

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