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Introduction to Computational Materials Science: Fundamentals to Applications [Hardback]

(Los Alamos National Laboratory)
  • Formāts: Hardback, 427 pages, height x width x depth: 252x196x22 mm, weight: 1060 g, 15 Tables, black and white; 339 Line drawings, black and white
  • Izdošanas datums: 28-Mar-2013
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521845874
  • ISBN-13: 9780521845878
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  • Formāts: Hardback, 427 pages, height x width x depth: 252x196x22 mm, weight: 1060 g, 15 Tables, black and white; 339 Line drawings, black and white
  • Izdošanas datums: 28-Mar-2013
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521845874
  • ISBN-13: 9780521845878
Citas grāmatas par šo tēmu:
Emphasising essential methods and universal principles, this textbook provides everything students need to understand the basics of simulating materials behaviour. All the key topics are covered from electronic structure methods to microstructural evolution, appendices provide crucial background material, and a wealth of practical resources are available online to complete the teaching package. Modelling is examined at a broad range of scales, from the atomic to the mesoscale, providing students with a solid foundation for future study and research. Detailed, accessible explanations of the fundamental equations underpinning materials modelling are presented, including a full chapter summarising essential mathematical background. Extensive appendices, including essential background on classical and quantum mechanics, electrostatics, statistical thermodynamics and linear elasticity, provide the background necessary to fully engage with the fundamentals of computational modelling. Exercises, worked examples, computer codes and discussions of practical implementations methods are all provided online giving students the hands-on experience they need.

Recenzijas

Finally, an introductory textbook on computational methods that addresses the breadth of materials science. Finally, an introductory textbook that emphasizes understanding the foundations of the subject. Kudos to Prof. Richard LeSar for producing such a beautifully pedagogical introductory text that covers the major methods of the field, relates them to their underlying science, and provides links to accessible simulation codes. "Introduction to Computational Materials Science" is the perfect companion to a first-course on this rapidly growing segment of our field. - David J Srolovitz, University of Pennsylvania Prof. LeSar has written an elegant book on the methods that have been found to be useful for simulating materials. Unlike most texts, he has made the effort to give clear, straightforward explanations so that readers can implement the models for themselves. He has also covered a wider range of techniques and length-/time-scales than typical textbooks that ignore anything coarser than the atom. This text will be useful for a wide range of materials scientists and engineers. - Anthony Rollett, Carnegie Mellon University Richard LeSar has successfully summarized the computational techniques that are most commonly used in Materials Science, with many examples that bring this field to life. I have been using drafts of this book in my Computational Materials course, with very positive student response. I am delighted to see the book in printit will become a classic! - Chris G. Van de Walle, University of California, Santa Barbara

Papildus informācija

Emphasising essential methods and universal principles, this textbook provides everything students need to understand the basics of simulating materials behaviour.
Preface xi
1 Introduction to materials modeling and simulation
1(10)
1.1 Modeling and simulation
1(1)
1.2 What is meant by computational materials science and engineering?
2(1)
1.3 Scales in materials structure and behavior
3(2)
1.4 How to develop models
5(2)
1.5 Summary
7(4)
PART ONE SOME BASICS
2 The random-walk model
11(16)
2.1 Random-walk model of diffusion
11(2)
2.2 Connection to the diffusion coefficient
13(5)
2.3 Bulk diffusion
18(1)
2.4 A random-walk simulation
19(6)
2.5 Random-walk models for materials
25(1)
2.6 Summary
26(1)
3 Simulation of finite systems
27(18)
3.1 Sums of interacting pairs of objects
27(2)
3.2 Perfect crystals
29(2)
3.3 Cutoffs
31(1)
3.4 Periodic boundary conditions
32(2)
3.5 Implementation
34(1)
3.6 Long-ranged potentials
35(1)
3.7 Summary
36(1)
3.8 Appendix
36(9)
PART TWO ATOMS AND MOLECULES
4 Electronic structure methods
45(17)
4.1 Quantum mechanics of multielectron systems
46(1)
4.2 Early density functional theories
47(4)
4.3 The Hohenberg-Kohn theorem
51(1)
4.4 Kohn-Sham method
51(3)
4.5 The exchange-correlation functional
54(1)
4.6 Wave functions
55(2)
4.7 Pseudopotentials
57(2)
4.8 Use of density functional theory
59(2)
4.9 Summary
61(1)
5 Interatomic potentials
62(34)
5.1 The cohesive energy
62(1)
5.2 Interatomic potentials
63(4)
5.3 Pair potentials
67(9)
5.4 Ionic materials
76(2)
5.5 Metals
78(6)
5.6 Covalent solids
84(4)
5.7 Systems with mixed bonding
88(1)
5.8 What we can simulate
89(2)
5.9 Determining parameters in potentials
91(1)
5.10 Summary
91(1)
5.11 Appendix
92(4)
6 Molecular dynamics
96(35)
6.1 Basics of molecular dynamics for atomic systems
96(11)
6.2 An example calculation
107(9)
6.3 Velocity rescaling
116(1)
6.4 Molecular dynamics in other ensembles
117(3)
6.5 Accelerated dynamics
120(2)
6.6 Limitations of molecular dynamics
122(1)
6.7 Molecular dynamics in materials research
123(2)
6.8 Summary
125(1)
6.9 Appendix
125(6)
7 The Monte Carlo method
131(27)
7.1 Introduction
131(1)
7.2 Ensemble averages
132(2)
7.3 The Metropolis algorithm
134(5)
7.4 The Ising model
139(6)
7.5 Monte Carlo for atomic systems
145(5)
7.6 Other ensembles
150(4)
7.7 Time in a Monte Carlo simulation
154(1)
7.8 Assessment of the Monte Carlo method
155(1)
7.9 Uses of the Monte Carlo method in materials research
155(1)
7.10 Summary
156(1)
7.11 Appendix
156(2)
8 Molecular and macromolecular systems
158(25)
8.1 Introduction
158(3)
8.2 Random-walk models of polymers
161(2)
8.3 Atomistic simulations of macromolecules
163(9)
8.4 Coarse-grained methods
172(3)
8.5 Lattice models for polymers and biomolecules
175(1)
8.6 Simulations of molecular and macromolecular materials
176(1)
8.7 Summary
177(1)
8.8 Appendix
178(5)
PART THREE MESOSCOPIC METHODS
9 Kinetic Monte Carlo
183(13)
9.1 The kinetic Monte Carlo method
183(4)
9.2 Time in the kinetic Monte Carlo method
187(2)
9.3 Kinetic Monte Carlo calculations
189(5)
9.4 Applications
194(1)
9.5 Summary
195(1)
10 Monte Carlo methods at the mesoscale
196(15)
10.1 Modeling Grain Growth
196(2)
10.2 The Monte Carlo Potts model
198(4)
10.3 The N-fold way
202(3)
10.4 Example applications of the Potts model
205(3)
10.5 Applications in materials science and engineering
208(2)
10.6 Summary
210(1)
11 Cellular automata
211(18)
11.1 Basics of cellular automata
211(4)
11.2 Examples of cellular automata in two dimensions
215(3)
11.3 Lattice-gas methods
218(1)
11.4 Examples of cellular automata in materials research
219(8)
11.5 Relation to Monte Carlo
227(1)
11.6 Summary
227(2)
12 Phase-field methods
229(20)
12.1 Conserved and non-conserved order parameters
229(1)
12.2 Governing equations
230(3)
12.3 A one-dimensional phase-field calculation
233(4)
12.4 Free energy of an interface
237(1)
12.5 Local free-energy functions
238(3)
12.6 Two examples
241(3)
12.7 Other applications in materials research
244(1)
12.8 Summary
244(1)
12.9 Appendix
244(5)
13 Mesoscale dynamics
249(20)
13.1 Damped dynamics
249(2)
13.2 Langevin dynamics
251(1)
13.3 Simulation "entities" at the mesoscale
252(1)
13.4 Dynamic models of grain growth
253(3)
13.5 Discrete dislocation dynamics simulations
256(9)
13.6 Summary
265(4)
PART FOUR SOME FINAL WORDS
14 Materials selection and design
269(12)
14.1 Integrated computational materials engineering
269(2)
14.2 Concurrent materials design
271(2)
14.3 Methods
273(2)
14.4 Materials informatics
275(3)
14.5 Summary
278(3)
PART FIVE APPENDICES
A Energy units, fundamental constants, and conversions
281(2)
A.1 Fundamental constants
281(1)
A.2 Units and energy conversions
281(2)
B A brief introduction to materials
283(27)
B.1 Introduction
283(1)
B.2 Crystallography
284(7)
B.3 Defects
291(1)
B.4 Point defects
291(1)
B.5 Dislocations
292(10)
B.6 Polycrystalline materials
302(4)
B.7 Diffusion
306(4)
C Mathematical backgound
310(14)
C.1 Vectors and tensors
310(4)
C.2 Taylor series
314(1)
C.3 Complex numbers
315(2)
C.4 Probability
317(1)
C.5 Common functions
318(3)
C.6 Functionals
321(3)
D A brief summary of classical mechanics
324(6)
D.1 Newton's equations
324(2)
D.2 The Hamiltonian
326(1)
D.3 Example: the harmonic oscillator
327(1)
D.4 Central-force potentials
328(2)
E Electrostatics
330(4)
E.1 The force
330(1)
E.2 Electrostatic potentials and energies
330(1)
E.3 Distribution of charges: the multipole expansion
331(3)
F Elements of quantum mechanics
334(17)
F.1 History
334(1)
F.2 Wave functions
335(1)
F.3 The Schrodinger equation
336(1)
F.4 Observables
336(1)
F.5 Some solved problems
337(5)
F.6 Atoms with more than one electron
342(4)
F.7 Eigenvalues and eigenvectors
346(2)
F.8 Multielectron systems
348(1)
F.9 Quantum mechanics of periodic systems
349(1)
F.10 Summary
349(2)
G Statistical thermodynamics and kinetics
351(24)
G.1 Basic thermodynamic quantities
351(1)
G.2 Introduction to statistical thermodynamics
352(1)
G.3 Macrostates versus microstates
352(1)
G.4 Phase space and time averages
353(2)
G.5 Ensembles
355(7)
G.6 Fluctuations
362(2)
G.7 Correlation functions
364(4)
G.8 Kinetic rate theory
368(6)
G.9 Summary
374(1)
H Linear elasticity
375(8)
H.1 Stress and strain
375(3)
H.2 Elastic constants
378(1)
H.3 Engineering stress and strain
378(1)
H.4 Isotropic solids
379(2)
H.5 Plastic strain
381(2)
I Introduction to computation
383(9)
I.1 Some basic concepts
383(1)
I.2 Random-number generators
383(3)
I.3 Binning
386(2)
I.4 Numerical derivatives
388(3)
I.5 Summary
391(1)
References 392(17)
Index 409
Richard LeSar is the Lynn Gleason Professor of Interdisciplinary Engineering in the Department of Materials Science and Engineering, Iowa State University, and the former Chair of the Materials Science and Engineering programme. He is highly experienced in teaching the modelling and simulation of materials at both undergraduate and graduate levels, and has made extensive use of these methods throughout his own research.