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Frenkel D., Smit B. — Understanding Molecular Simulation: from algorithms to applications
Frenkel D., Smit B. — Understanding Molecular Simulation: from algorithms to applications

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Название: Understanding Molecular Simulation: from algorithms to applications

Авторы: Frenkel D., Smit B.

Аннотация:

Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulators are continuously confronted with questions concerning the choice of a particular technique for a given application. A wide variety of tools exist, so the choice of technique requires a good understanding of the basic principles. More importantly, such understanding may greatly improve the efficiency of a simulation program. The implementation of simulation methods is illustrated in pseudocodes and their practical use in the case studies used in the text.


Язык: en

Рубрика: Математика/Численные методы/Моделирование физических процессов/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: second edition

Год издания: 2002

Количество страниц: 638

Добавлена в каталог: 21.02.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
Abraham, KF.      551
Abramowitz, M.      260
Acceptance rule, biased sampling      323
Acceptance rule, canonical ensemble      29 32 113
Acceptance rule, CBMC fixed endpoints      355
Acceptance rule, configurational-bias Monte Carlo      332 334 339
Acceptance rule, Gibbs ensemble      205
Acceptance rule, Gibbs ensemble technique      372
Acceptance rule, grand-canonical ensemble      130 367
Acceptance rule, isobaric-isothermal ensemble      118
Acceptance rule, Metropolis scheme      29
Acceptance rule, NPT ensemble      118
Acceptance rule, orientational bias      324
Acceptance rule, parallel tempering      390
Acceptance rule, path ensemble      455
Acceptance rule, semigrand ensemble      230
Acceptance-rejection technique      341
Accepting a trial move      30
Ackland, G.J.      261 262 263
Activation-relaxation technique      463
Adams, D.J.      1ll 128 178 257 258
Adiabatic transformation      172
Adolf, D.B.      316 317
Adsorption, example      134
Adsorption, methane in zeolite      135
Agrawal, R.      168 234 236
Ailawadi, N.K.      527 529
Alder, B.J.      4 6 167 235 237 257 263 266 478
Alexander, F.J.      478
Alexandrowicz, Z.      375
Algorithm      xvi
Algorithm, cell lists      551—553
Algorithm, cell lists and Verlet lists      554 555
Algorithm, combined lists      554 555
Algorithm, configurational-bias Monte Carlo      344 346 347
Algorithm, configurational-bias Monte Carlo (lattice)      334 335
Algorithm, diffusion      91 95
Algorithm, equations of motion: Andersen thermostat      144
Algorithm, equations of motion: Nose — Hoover thermostat      540—542
Algorithm, equations of motion: Verlet algorithm      70
Algorithm, exchange of particle      132
Algorithm, force, calculation of the      68
Algorithm, Gaussian distribution      579
Algorithm, generate an Einstein crystal      252
Algorithm, generate bond and torsion angle      580
Algorithm, generate bond angle      579
Algorithm, generate bond length with harmonic springs      578
Algorithm, Gibbs ensemble technique      209 210 212
Algorithm, growing a chain on a lattice      335
Algorithm, growing an alkane      344
Algorithm, growing ethane      346
Algorithm, growing propane      346
Algorithm, initialization      66
Algorithm, linked lists      551—553
Algorithm, mean-squared displacement      91 95
Algorithm, Molecular Dynamics: Andersen thermostat      143
Algorithm, Molecular Dynamics: Nose — Hoover thermostat      540—542
Algorithm, Molecular Dynamics: NVE ensemble      65
Algorithm, Monte Carlo technique (NVT)      251
Algorithm, Monte Carlo technique: $\mu VT$ ensemble      131 132
Algorithm, Monte Carlo technique: (fixed center of mass)      251
Algorithm, Monte Carlo technique: NPT ensemble      121 122
Algorithm, Monte Carlo technique: NVT ensemble      33
Algorithm, multiple time step      426
Algorithm, orientational bias      325
Algorithm, particle displacement      33
Algorithm, particle displacement (fixed center of mass)      251
Algorithm, particle exchange (Gibbs)      212
Algorithm, particle insertion method      175
Algorithm, radial distribution function      86
Algorithm, random vector on a unit sphere      578
Algorithm, selection of trial orientations      577
Algorithm, trial position of n-alkane      347
Algorithm, velocity autocorrelation function      91 95
Algorithm, Verlet      82
Algorithm, Verlet lists      547—549
Algorithm, volume change (Gibbs)      210
Algorithm, volume change (NPT)      122
Algorithm, Widom method      175
Alkanes, critical properties      372
Alkanes, example      280 368
Alkanes, generation of trail positions      342
Allen, M.P.      6 30 39 48 49 58 63 85 216 277 397 421 467 510 529 578 584
Amar,J.G.      223
Amon, L.M.      55
Andersen thermostat, algorithm      143 144
Andersen thermostat, case study      142
Andersen thermostat, exercise      161
Andersen thermostat, harmonic oscillator      155
Andersen thermostat, Lennard — Jones      142
Andersen, H.C.      75 125 139 141 142 144 146 147 159 267
Anderson, H.L.      27
Anselme, M.J.      373
Antisymmetric matrix      490
Appel,A.W.      306
Attard, P.      117
Auer,S.      394 396 462
Auerbach, D.J.      551
Azadipour, A.Z.      389
Azhar,F.E.      234
Backx,G.      469 473
Bakker,A.F      472 551
Balian, R.      15
Banaszak, B.J.      374
Barber, M.      316 317
Barkema, G.T.      6 463
Barker, J.A.      4 236 243
Barnes, J.      306
Barrier crossing, case study      440
Bastolla,U.      286
Bates, M.A.      267 523
Batoulis, J.      280 281 283 331
Baus, M.      6
Beckers, J.V.L.      312
Beeman algorithm      76
Bekker,H.      546 550
Bell,A.T.      135 281 282
Bennet — Chandler approach      436
Bennett, C.H.      179 187 189 263 266 432 575
Benzi,R.      476 477
Berendsen, H.J.C.      77 161 162 172 414 427 546 550
Berens,P.H.      75 144
Berg,B.A.      262
Berkowitz, M.L.      311 313 314 317 318
BernaLJ.D.      3 4
Berne, B.J.      77 316 398 409 432 462 584
Biben,T.      361 363
Binder, K.      6 125 167 181 217 218 219 280 331 399 523
Bird,G.A.      477 478
Bladon,P      361
Blander, M.      423
Bolhuis, PG.      202 234 237 239 261 361 363 364 365 450 453 456
Bond formation scheme      405
Bond formation scheme, acceptance rule      405
Bonded potential energy      337
Bonet Avalos, J.      473 474
Boone, T.D.      51 353 358 359 360
boundary conditions      32
Bowles, R.K.      266
Branched alkanes, configurational-bias Monte Carlo      350
Brey,J.J.      439 447 450
Brodka,A.      316 317
Brooks, B.R.      291
Broughton, J.Q.      244
Brown, B.C.      236 243
Brownian dynamics      474
Bruce, A.D.      125 217 261 262 263 395
Bruin, C.      472
Buff,F.P      472
Bunker, A.      394 397
Caillol,J.-M.      222 292 330
Camp, P.J.      222
Canonical ensemble, , Monte Carlo technique, justification of      114
Canonical ensemble, Monte Carlo technique      112
Canonical transformation, symplectic condition      491
Cape, J.N.      167 236
Car,R.      410 421
Case study      xvi
Case Study, $\mu VT$ ensemble      133
Case Study, Andersen thermostat      142
Case Study, barrier crossing      440
Case Study, cell lists      554
Case Study, chemical potential: Lennard — Jones      175 181
Case Study, comparison CPU saving schemes      554
Case Study, configurational-bias Monte Carlo      340 345
Case Study, constraints      427
Case Study, detailed balance      54
Case Study, diffusion      100 101
Case Study, Dissipative particle dynamics      470
Case Study, dynamic properties of the Lennard — Jones fluid      100
Case Study, Einstein crystal      256
Case Study, equation of state: Lennard — Jones      51 122 133
Case Study, equation of state: Lennard — Jones chains      340
Case Study, Gibbs ensemble technique      211
Case Study, hard spheres      256
Case Study, harmonic oscillator      155 157
Case Study, keep old configuration      56
Case Study, Lennard — Jones      51 54 56 98 100 101 122 123 133 142 153 175 181 211
Case Study, Molecular Dynamics      98 100 101
Case Study, Monte Carlo technique      51 54 56 122 123 133 211 256
Case Study, multiple time step      427
Case Study, Nose — Hoover thermostat      153
Case Study, NPT ensemble      122 123
Case Study, NVT ensemble      51
Case Study, overlapping distribution      181
Case Study, parallel tempering      391
Case Study, particle insertion method      175
Case Study, path ensemble      456
Case Study, phase equilibria: Lennard — Jones      123 211
Case Study, rare events      440 456
Case Study, recoil growth      382
Case Study, SHAKE      427
Case Study, solid-liquid phase equilibrium of hard spheres      256
Case Study, static properties of the Lennard — Jones fluid      98
Case Study, trial configurations of ideal chains      345
Case Study, Verlet lists      554
Case Study, Widom method      175
Catlow,C.R.A      134
Cell lists      550
Cell lists, algorithm      551—553
Cell lists, case study      554
Chain molecules, chemical potential      270
Chain molecules, concerted rotation      51
Chain molecules, example      396
Chandler, D.      193 353 403 404 432 443 450 453 456 462 509
Chao,K.C.      128
Chemical potential, acceptance ratio method      189
Chemical potential, case study      175 181
Chemical potential, chain molecules      270
Chemical potential, excess chemical potential      174 211
Chemical potential, finite-size corrections      178
Chemical potential, Gibbs ensemble      211
Chemical potential, ideal gas      129 560
Chemical potential, incremental      270
Chemical potential, Lennard — Jones      175 181
Chemical potential, mixtures      226
Chemical potential, modified Widom method      270
Chemical potential, multiple-histograms      183
Chemical potential, NPT ensemble      177
Chemical potential, NVE ensemble      178
Chemical potential, NVT ensemble      174
Chemical potential, overlapping distribution      179 282
Chemical potential, particle insertion method      173 174
Chemical potential, recursive sampling      283
Chemical potential, Rosenbluth sampling      279
Chemical potential, self-consistent histogram method      184
Chemical potential, tail correction      176
Chemical potential, thermodynamic integration      269
Chemical potential, umbrella sampling      192
Chemical potential, Widom method      173 174
Chen, B.      345 374
Chen,S.      476 477
Chen,Z.      357
Cho,K.      501
Chung, S.T.      178
Ciccotti, G.      3 6 50 51 63 156 176 178 226 253 414 421 427 432 437 440 443 462 495 497 507 510 516
Clarke, J.H.R.      316 317
Clausius — Clapeyron equation      233
Cluster moves, example      403
Coarse-grained model      465
Cochran, H.D.      374
Cohen, L.K.      128
Coker,D.F.      432 462
Colloids      465
Colloids, example      363
Compressibility, phase space      496
Concerted rotation      51 357
Configurational-bias Monte Carlo acceptance rule      332 334 339
Configurational-bias Monte Carlo acceptance rule, algorithm (alkane)      344 347
Configurational-bias Monte Carlo acceptance rule, algorithm (ethane)      346
Configurational-bias Monte Carlo acceptance rule, algorithm (lattice)      334 335
Configurational-bias Monte Carlo acceptance rule, algorithm (propane)      346
Configurational-bias Monte Carlo acceptance rule, branched alkanes      350
Configurational-bias Monte Carlo acceptance rule, case study      340 345
Configurational-bias Monte Carlo acceptance rule, exercise      384 386
Configurational-bias Monte Carlo acceptance rule, explicit-hydrogen model      345
Configurational-bias Monte Carlo acceptance rule, fixed endpoints (continuum)      355
Configurational-bias Monte Carlo acceptance rule, fixed endpoints (lattice)      353
Configurational-bias Monte Carlo acceptance rule, Gibbs ensemble technique      370
Configurational-bias Monte Carlo acceptance rule, justification (lattice)      334
Configurational-bias Monte Carlo acceptance rule, justification (off-lattice)      339
Configurational-bias Monte Carlo acceptance rule, lattice      332
Configurational-bias Monte Carlo acceptance rule, off-lattice      336
Configurational-bias Monte Carlo acceptance rule, super-detailed balance      340
Configurational-bias Monte Carlo acceptance rule, trial orientations      341
Conformational-bias Monte Carlo, Recoil growth, versus      374
Consta, S.      375 382
Constrained dynamics, averages      415
Constrained dynamics, case study      427
Constrained dynamics, probability density      41
Constrained dynamics, SHAKE      427
Coordinate transformation, canonical      489
Coulomb potential      292
Cracknell, R.F.      329
Creutz,M.      111 114
Crippen, G.M.      399
Critical exponents      217
Crooks, G.E.      196 198
Crozier,P.S.      318
Csajka,F.S.      450 456
Cui,S.T.      374
Cummings, P.T.      168 374
Darden, T.A.      292 311 312 313 314 316
Davis, H.T.      135 221
De Gennes, P.G.      222
de Leeuw, S.W.      170 222 292 312 317 415
de Miguel, E.      220 223
de Pablo, J.J.      235 271 331 372 374 394 395 397
de Smedt, Ph.      168 216 218 567
de Swaan Arons, J      223
Deem, M.W.      42 358 359 360 386 393
Deitrick, G.L.      221
Dellago, C      450 453 456 462
Deserno,M.      311 312 314
Detailed balance      42 112
Detailed balance, biased configurations      323
Detailed balance, canonical ensemble      114
Detailed balance, case study      54
Detailed balance, grand-canonical ensemble      130
Detailed balance, Metropolis scheme      29
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