Yip / Rubia Scientific Modeling and Simulations

1. Auflage 2010
ISBN: 978-1-4020-9741-6
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 68, 402 Seiten

Reihe: Lecture Notes in Computational Science and Engineering

Although computational modeling and simulation of material deformation was initiated with the study of structurally simple materials and inert environments, there is an increasing demand for predictive simulation of more realistic material structure and physical conditions. In particular, it is recognized that applied mechanical force can plausibly alter chemical reactions inside materials or at material interfaces, though the fundamental reasons for this chemomechanical coupling are studied in a material-speci c manner. Atomistic-level s- ulations can provide insight into the unit processes that facilitate kinetic reactions within complex materials, but the typical nanosecond timescales of such simulations are in contrast to the second-scale to hour-scale timescales of experimentally accessible or technologically relevant timescales. Further, in complex materials these key unit processes are 'rare events' due to the high energy barriers associated with those processes. Examples of such rare events include unbinding between two proteins that tether biological cells to extracellular materials [1], unfolding of complex polymers, stiffness and bond breaking in amorphous glass bers and gels [2], and diffusive hops of point defects within crystalline alloys [3].

Autoren/Hrsg.

Yip, Sidney

Rubia, Tomas Diaz

Inhaltsverzeichnis

1;Contents;5
2;Scientific Modeling and Simulations;7
3;A retrospective on the journal of computer-aided materials design (JCAD), 1993--2007;9
4;Extrapolative procedures in modelling and simulations: the role of instabilities;11
4.1;Abstract;11
4.2;1 Introduction;11
4.2.1;1.1 Superconductivity;12
4.2.2;1.2 Low-temperature heat capacity;13
4.2.3;1.3 Crystals modelled as stacking of atomic spheres;14
4.3;2 Elastic shear instability and melting;15
4.4;3 Shear constants in alloys;16
4.5;4 Melting of superheated solids;18
4.6;5 Theoretical strength of solids;21
4.7;6 Another model---the linear chain;22
4.8;7 Four types of extrapolative procedures;23
4.9;8 Conclusions;24
4.10;Acknowledgements;24
4.11;References;24
5;Characteristic quantities and dimensional analysis;27
5.1;Abstract;27
5.2;1 Introduction;27
5.3;2 Four examples of characteristic quantities;28
5.3.1;2.1 Waves at sea;28
5.3.2;2.2 Temperature profile in the ground;29
5.3.3;2.3 Terminal velocity;29
5.3.4;2.4 Engineering science versus school physics;30
5.4;3 Waves revisited;31
5.5;4 Characteristic quantities;31
5.6;5 Buckingham's . theorem;33
5.7;6 Scaling;35
5.8;7 Systematics in the neglect of certain effects;35
5.8.1;7.1 Gravity waves in deep water;35
5.8.2;7.2 Small hole in a large sheet under stress;36
5.9;8 The Lennard-Jones model;37
5.10;9 The Lindemann melting criterion;39
5.11;10 Saturating conductivities -- a still unsolved problem;40
5.12;11 Conclusions;42
5.13;Appendix 1;42
5.13.1;1.1 Thermal conduction in a semi-infinite medium;42
5.13.2;1.2 Falling sphere with air resistance;43
5.14;Appendix 2;43
5.14.1;2.1 Spider silk;43
5.14.2;2.2 Rayleigh instability and Bénard cells;44
5.15;References;44
6;Accuracy of models;46
6.1;Abstract;46
6.2;1 Introduction;46
6.3;2 Robustness;47
6.3.1;2.1 The oil peak problem;47
6.3.2;2.2 The entropy of TiC;48
6.3.3;2.3 Discussion;50
6.4;3 Fitting of data---two different objectives;51
6.4.1;3.1 The CALPHAD method;51
6.4.2;3.2 Separation of contributions to the heat capacity;52
6.4.3;3.3 Discussion;53
6.5;4 Fitting in log-log plots;55
6.5.1;4.1 Introduction;55
6.5.2;4.2 Thermal conduction in insulators;55
6.6;5 Second-order effects;56
6.6.1;5.1 Introduction;56
6.6.2;5.2 Weakly inhomogeneous materials;56
6.7;6 Mislead by simple textbook results;57
6.7.1;6.1 The vibrational heat capacity;57
6.7.2;6.2 The electronic heat capacity;58
6.8;7 Conclusions;59
6.9;Appendix 1;60
6.9.1;Moment frequencies and thermodynamic functions;60
6.10;Appendix 2;60
6.10.1;Sum of power laws in a log-log plot;60
6.11;Appendix 3;61
6.11.1;Power law versus exponential behavior;61
6.12;References;61
7;Multiscale simulations of complex systems: computation meets reality;63
7.1;Abstract;63
7.2;1 Introduction;63
7.3;2 Two representative examples;64
7.4;3 Problems and prospects;67
7.5;Acknowledgements;68
7.6;References;68
8;Chemomechanics of complex materials: challenges and opportunities in predictive kinetic timescales;70
8.1;Abstract;70
8.2;1 Introduction;71
8.3;2 Putting rare events and rough energy landscapes in context of real materials;71
8.3.1;2.1 Rare events and rough energy landscapes;71
8.3.2;2.2 Diving in to rough energy landscapes of alloys, glasses, and biomolecules;73
8.4;3 Forced unbinding of biomolecular complexes;76
8.4.1;3.1 Does stiffness matter? Why ks perturbs the accessible molecular rupture forces;76
8.4.2;3.2 Enough is enough: practical requirements of rare event sampling in MD;78
8.5;4 Potential advances for chemomechanical analysis of other complex materials;79
8.6;5 Summary and outlook;81
8.7;References;82
9;Tight-binding Hamiltonian from first-principles calculations;84
9.1;1 Introduction;84
9.2;2 Quasi-atomic minimal-basis-sets orbitals;86
9.3;3 Tight-binding matrix elements in terms of QUAMBOs;89
9.4;4 Large-scale electronic calculations using the QUAMBO scheme;92
9.5;5 Concluding remarks;96
9.6;Acknowledgements;97
9.7;References;98
10;Atomistic simulation studies of complex carbon and silicon systems using environment-dependent tight-binding potentials;99
10.1;1 Introduction;99
10.2;2 Environment-dependent tight-binding potential model;100
10.2.1;2.1 General formalism of tight-binding potential model;100
10.2.2;2.2 EDTB potential model formalism;101
10.3;3 EDTB potential for carbon and its applicationa;103
10.3.1;3.1 EDTB potential for carbon;103
10.3.2;3.2 TBMD simulation of vacancy diffusion and reconstruction in grapheme;105
10.3.3;3.3 TBMD simulation of junction formation in carbon nanotubes;108
10.4;4 EDTB potential for silicon and its applications;110
10.4.1;4.1 EDTB potential for silicon;110
10.4.2;4.2 TBMD simulation studies of addimer diffusion on Si(100) surface;111
10.4.2.1;4.2.1 Diffusion between trough and the top of dimer row;115
10.4.2.2;4.2.2 Diffusion along the trough between the dimmer rows;117
10.4.3;4.3 TBMD study of dislocation core structure in Si;119
10.5;5 Future perspective;121
10.6;Acknowledgment;121
10.7;References;122
11;First-principles modeling of lattice defects: advancing our insight into the structure-properties relationship of ice;124
11.1;Abstract;124
11.2;1 Introduction;124
11.3;2 Molecular point defects;129
11.4;3 Bjerrum defect/molecular point defect interactions;135
11.5;4 Summary;140
11.6;Acknowledgements;141
11.7;References;141
12;Direct comparison between experiments and computations at the atomic length scale: a case study of graphene;143
12.1;Abstract;143
12.2;1 Introduction;143
12.3;2 Overview of multi-scale simulations in ductile metals;144
12.4;3 Mechanical experiments at small length scales;145
12.5;4 Mechanical experiments on monolayer graphene;147
12.6;5 Analysis of experiments;151
12.7;6 Suggestions for further simulations;153
12.8;7 Conclusions;154
12.9;Acknowledgements;155
12.10;References;155
13;Shocked materials at the intersection of experiment and simulation;158
13.1;Abstract;158
13.2;1 Introduction;159
13.3;2 Approaches to in situ studies of atomic processes under dynamic compression;160
13.3.1;2.1 X-ray techniques---Diffraction and scattering;161
13.3.1.1;2.1.1 Laser-based systems for x-ray diffraction;161
13.3.1.2;2.1.2 Accelerator-based light source for x-ray scattering;162
13.3.1.3;2.1.3 Computation-simulation;162
13.4;3 Materials response to shock loading;163
13.4.1;3.1 Inelastic response to shock loading (1D to 3D transition);163
13.4.2;3.2 Phase transformations;169
13.4.2.1;3.2.1 Phase transition pathways;169
13.4.2.2;3.2.2 Phase transition under uniaxial shock compression along [001]BCC direction;170
13.4.2.3;3.2.3 Calculated observables for the a--e phase transition;170
13.4.2.4;3.2.4 In situ, Real-time diffraction measurements during the shock;172
13.4.2.5;3.2.5 The transformation mechanism;173
13.5;4 Future work;175
13.5.1;4.1 Dynamic melt: simulation and experiment;175
13.5.2;4.2 Damage: in situ void nucleation and growth;177
13.6;5 Conclusion;180
13.7;Acknowledgements;182
13.8;References;182
14;Calculations of free energy barriers for local mechanisms of hydrogen diffusion in alanates;186
14.1;Abstract;186
14.2;1 Introduction;186
14.3;2 Model and computations;188
14.3.1;2.1 The simulation set up and its validation;190
14.3.2;2.2 Collective variables for the local H-vacancy diffusion;191
14.3.3;2.3 Collective variables for the non-local H-vacancy diffusion;194
14.4;3 Methods;194
14.4.1;3.1 Temperature accelerated molecular dynamics;195
14.4.2;3.2 Radial basis representation of the free energy;196
14.5;4 Results;197
14.5.1;4.1 Local hydrogen diffusion;197
14.5.2;4.2 Non-local hydrogen diffusion;199
14.5.2.1;4.2.1 The TAMD trajectory;199
14.5.2.2;4.2.2 Radial basis reconstruction of the free energy;200
14.5.2.3;4.2.3 Calculation of the activation barrier;203
14.6;5 Conclusions;204
14.7;Acknowledgements;204
14.8;References;204
15;Concurrent design of hierarchical materials and structures;206
15.1;Abstract;206
15.2;1 Introduction;206
15.3;2 What is materials design?;214
15.3.1;2.1 Hierarchy of scales in concurrent design of materials and products;215
15.3.2;2.2 Goals of materials design;218
15.4;3 Some aspects of systems approaches for materials design;220
15.4.1;3.1 Role of thermodynamics;220
15.4.2;3.2 Challenges for top-down, inductive design;220
15.4.3;3.3 Uncertainty in materials design;221
15.4.4;3.4 Microstructure-mediated design;224
15.5;4 Applications of materials design;225
15.5.1;4.1 High strength and toughness steels;226
15.5.2;4.2 Integrating advances in 3D characterization and modeling tools;228
15.6;5 Educational imperatives for materials design;230
15.7;6 Future prospects;232
15.8;Closure;234
15.9;Acknowledgements;234
15.10;References;235
16;Enthalpy landscapes and the glass transition;240
16.1;Abstract;240
16.2;1 Introduction;241
16.3;2 The glass transition;242
16.4;3 The enthalpy landscape approach;246
16.4.1;3.1 Potential energy landscapes;247
16.4.2;3.2 Enthalpy landscapes;250
16.4.3;3.3 Nonequilibrium statistical mechanics;252
16.5;4 Simulation techniques;253
16.5.1;4.1 Locating inherent structures and transition points;254
16.5.2;4.2 Inherent structure density of states;259
16.5.3;4.3 Master equation dynamics;260
16.6;5 Nature of the glassy state;263
16.6.1;5.1 Continuously broken ergodicity and the residual entropy of glass;263
16.6.2;5.2 Supercooled liquid fragility;267
16.6.3;5.3 The Kauzmann paradox and the ideal glass transition;269
16.6.4;5.4 Fictive temperature and the glassy state;274
16.7;6 Conclusions;278
16.8;Acknowledgements;278
16.9;References;278
17;Advanced modulation formats for fiber optic communication systems;281
17.1;Abstract;281
17.2;1 Introduction and background;281
17.3;2 Modulation techniques;282
17.3.1;2.1 The electro-optic effect;282
17.3.2;2.2 Phase modulators;283
17.3.3;2.3 Amplitude modulators;283
17.3.3.1;2.3.1 Mach-Zehnder modulation with an ideal branching ratio;284
17.3.3.2;2.3.2 Mach-Zehnder modulation with a non-ideal branching ratio;286
17.3.3.3;2.3.3 Calculation of extinction ratio;287
17.3.3.4;2.3.4 Chirp induced by Mach-Zehnder modulation;288
17.4;3 Modulation formats;290
17.4.1;3.1 Nonreturn-to-zero (NRZ);290
17.4.2;3.2 Return-to-zero (RZ);291
17.4.2.1;3.2.1 RZ with 50% duty cycle;292
17.4.2.2;3.2.2 RZ with 33% duty cycle;294
17.4.2.3;3.2.3 Carrier-suppressed RZ (CSRZ) with 67% duty cycle;296
17.4.2.4;3.2.4 Chirped RZ (CRZ);298
17.4.3;3.3 Duobinary;299
17.4.4;3.4 Modified duobinary;300
17.4.5;3.5 Differential phase-shift keyed (DPSK);301
17.4.6;3.6 Return-to-zero DPSK (RZ-DPSK);304
17.5;4 Impact on system performance;304
17.5.1;4.1 Amplified spontaneous emission (ASE) noise;306
17.5.2;4.2 Fiber nonlinearities;307
17.5.3;4.3 Linear cross-talk;308
17.5.4;4.4 Chromatic dispersion;308
17.5.5;4.5 Polarization mode dispersion (PMD);308
17.6;5 Conclusions;308
17.7;Acknowledgements;309
17.8;References;309
18;Computational challenges in the search for and production of hydrocarbons;311
18.1;Abstract;311
18.2;1 Introduction;311
18.3;2 Part I. The big picture---geophysical imaging and inversion;312
18.3.1;2.1 Seismic imaging for exploration and production;312
18.3.2;2.2 Towards inversion for reservoir properties;316
18.3.3;2.3 Evolution to 4-D (time lapse) seismic and reservoir simulations;317
18.3.4;2.4 Inversion of seismic and electromagnetic wavefields;319
18.4;3 Part II. Formation evaluation---pore scale fundamentals of oil recovery ;321
18.4.1;3.1 Borehole measurements;321
18.4.2;3.2 Drilling and geosteering;321
18.4.3;3.3 Porescale physics;322
18.4.3.1;3.3.1 Introduction;322
18.4.3.2;3.3.2 What do we want to achieve?;323
18.4.3.3;3.3.3 Porescale simulations;325
18.5;4 Part III. Simulation for new enabling technologies in the oil and gas industry;329
18.5.1;4.1 Introduction;329
18.5.2;4.2 Illuminating the oilfield with new sensor systems;329
18.5.3;4.3 Computational materials;330
18.5.3.1;4.3.1 High temperature polymer composites;331
18.6;5 Summary;332
18.7;Acknowledgements;334
18.8;References;334
19;Microscopic mechanics of biomolecules in living cells;336
19.1;Abstract;336
19.2;1 Cell mechanics and adhesion;340
19.3;2 Modelling molecules inside cells;344
19.4;3 Mechanical loading of single molecules;347
19.5;4 Nanomechanics of living polymers;350
19.6;5 Perspectives: physics, mechanics, and the multiscale modelling of biomolecules;352
19.7;References;356
20;Enveloped viruses understood via multiscale simulation: computer-aided vaccine design;360
20.1;Abstract;360
20.2;1 Introduction;361
20.3;2 Order parameters for connected structures;364
20.4;3 Order parameter fields for disconnected subsystems;366
20.5;4 Multiscale integration for enveloped virus modeling;367
20.6;5 Multiscale computations and the NanoX platform;371
20.7;6 Applications and conclusions;373
20.8;Acknowledgements;376
20.9;References;376
21;Computational modeling of brain tumors: discrete, continuum or hybrid?;378
21.1;Abstract;378
21.2;1 Introduction;379
21.3;2 In silico brain tumor modeling: objectives & challenges;380
21.4;3 Computational modeling approaches;381
21.4.1;3.1 Discrete modeling;381
21.4.2;3.2 Continuum modeling;383
21.5;4 Conclusions and perspectives;385
21.6;Acknowledgements;387
21.7;References;387
22;Editorial Policy;391
23;General Remarks;392
24;Lecture Notes in Computational Science and Engineering;393
25;Monographs in Computational Science and Engineering;395
26;Texts in Computational Science and Engineering;396

Leseproben

"Enveloped viruses understood via multiscale simulation: computer-aided vaccine design (p. 363-364)

Z. Shreif · P. Adhangale · S. Cheluvaraja · R. Perera · R. Kuhn · P. Ortoleva

Abstract Enveloped viruses are viewed as an opportunity to understand howhighly organized and functional biosystems can emerge from a collection ofmillions of chaotically moving atoms. They are an intermediate level of complexity between macromolecules and bacteria. They are a natural system for testing theories of self-assembly and structural transitions, and for demonstrating the derivation of principles of microbiology from laws of molecular physics. As some constitute threats to human health, a computer-aided vaccine and drug design strategy that would follow from a quantitative model would be an important contribution. However, current molecular dynamics simulation approaches are not practical for modeling such systems.

Our multiscale approach simultaneously accounts for the outer protein net and inner protein/genomic core, and their less structured membranous material and host fluid. It follows from a rigorous multiscale deductive analysis of laws of molecular physics. Two types of order parameters are introduced: (1) those for structures wherein constituent molecules retain long-lived connectivity (they specify the nanoscale structure as a deformation from a reference configuration) and (2) those for which there is no connectivity but organization is maintained on the average (they are field variables such as mass density or measures of preferred orientation). Rigorous multiscale techniques are used to derive equations for the order parameters dynamics. The equations account for thermal-average forces, diffusion coefficients, and effects of random forces. Statistical properties of the atomic-scale fluctuations and the order parameters are co-evolved. By combining rigorous multiscale techniques and modern supercomputing, systems of extreme complexity can be modeled.

Keywords Enveloped viruses · Structural transitions · All-atom multiscale analysis · Multiscale computation · Liouville equation · Langevin equations

1 Introduction

Deriving principles of microbial behavior from laws of molecular physics remains a grand challenge. While one expects many steps in the derivation can be accomplished based on the classical mechanics of an N-atom system, it is far from clear howto proceed in detail due to the extreme complexity of these supra-million atom systems.

Most notably, molecular dynamics (MD) codes are not practical for simulating even a simple bionanosystem of about 2 million atoms (e.g. a nonenveloped virus) over biologically relevant time periods (i.e. milliseconds or longer). For example, the efficientMDcode NAMD, run on a 1024-processor supercomputer [1], would take about 3000 years to simulate a simple virus over a millisecond; the largest NAMD simulation published to date is for a ribosome system of approximately 2.64 million atoms over few nanoseconds only [2].

We hypothesize that a first step in the endeavor to achieve a quantitative, predictive virology is to establish a rigorous intermediate scale description. Due to their important role in human health, complex structure, and inherent multiscale nature, enveloped viruses provide an ideal system for guiding and testing this approach. Experimental evidence suggests that an enveloped virus manifests three types of organization:"