• oo, 0 -* 0 (for the realistic model of a polymer in solution a different dynamics would then result, described by the Zimm model,141 due to hydrodynamic forces mediated by the solvent molecules
28
INTRODUCTION
(see also Chapter 3).7'17'84 Only in the absence of solvent molecules may the Rouse model result also from an MD simulation83). Another universal regime results for dense melts: then it is the entanglement molecular weight Ne of the different models that needs to be mapped for a quantitative comparison (see also Chapter 4).7>85'127 Since Figs 1.9(a), (b) refer to solution while Fig. 1.9(c) refers to a melt, one should not expect more than a similarity of qualitative character here. Since chains renew their configuration only on time scales larger than these characteristic times T\, TI, TI, r$, Fig. 1.9(c) provides practical evidence for our estimates of section 1.1.2, that times exceeding a nanosecond are needed to equilibrate melts of nonentangled short chains at high temperatures. Only for the coarse-grained models can one so far estimate the variation of the relaxation times over a significantly wide range of N, N (Fig. 1.10). One finds the expected power-law behavior for both models. A particularly interesting feature is found when one compares the absolute value of the relaxation times for the same chain length: e.g., for N = 3Q we have r\ K> 1200 in the off-lattice model but r\ ~ 3600 in the bond fluctuation model. Thus the off-lattice model needs a factor of three less MCS to reach the same physical relaxation time. This fact partially offsets the disadvantage that the off-lattice algorithm performs distinctly slower. Thus the general conclusion of this section is that one must think carefully about the conversion of scales (for length, time, molecular weight) when one compares physical results from different models, or the efficiency of various algorithms. It is hoped that the above examples serve as a useful guideline of how to proceed in practice. 1.4 Selected issues on computational techniques
In this section are briefly reviewed some technical problems of the simulation of dense many-chain systems, such as the sampling of intensive variables such as chemical potential, pressure etc., but also entropy, which are not straightforward to obtain as averages of "simple" quantities. Some of the standard recipes developed for computer simulation of condensed phases in general8"13 have difficulties here, due to the fact that the primary unit, the polymer chain, is already a large object and not a point particle. But knowledge of quantities such as the chemical potentials are necessary, e.g., for a study of phase equilibria in polymer solutions.42 1.4.1 Sampling the chemical potential in NVT simulations This problem has been brilliantly reviewed by Kumar in a recent book142 and hence we summarize only the most salient features here. For small molecule systems, sampling of the chemical potential rests on the Widom test particle insertion method143
S E L E C T E D ISSUES ON C O M P U T A T I O N A L T E C H N I Q U E S 29
Fig. 1.10 Log-log plot of relaxation time T\ vs. chain length N, for the bead-spring model with soft Lennard-Jones repulsion76 and the bond fluctuation model.128 Open circles (and left ordinate scale) refer to off-lattice model at = 0.0625, full dots (and right scale) to the bond fluctuation model at = 0.05 (data taken from Ref. 128). Straight lines indicated the power laws r\ oc N2, where the exponent z = 2.3 or 2.24, respectively, is reasonably compatible with the theoretical prediction16- z = 2 ^ + 1 KI 2.18. Insert shows the ratio T$/T\. It is seen that both models give mutually compatible results for the JV-dependence of this dimensionless ratio (which should settle down at some universal constant for N —> oo). In this figure the distinction between N and N (which is only a rather small shift on the logarithmic scale) is disregarded. (From Gerroff et a/..76)
where A/x is the chemical potential difference relative to the chemical potential of an ideal gas at a same temperature and density, F^- is the Helmholtz free energy of a system containing Jf particles, and { ... )^ represents a canonical ensemble average. This test particle insertion method involves the insertion of a ghost particle into a frozen equilibrium snapshot of a system containing Jf particles, and U denotes the total potential energy experienced by this test particle. Averaging the appropriate Boltzmann factor over many different configurations (frozen snapshots) of the system, the chemical potential is obtained from eq. (1.18), and this method works
30
INTRODUCTION
well in practice for small molecule fluids (for examples see Refs 144, 145). Now, for polymers the insertion of a polymer in a frozen equilibrium snapshot has a very low acceptance probability, and this probability decreases exponentially with increasing chain length. Hence this method has been restricted to N < 20 for lattice models146'147 and to N < 15 for pearl necklace off-lattice chains.148"151 Several schemes have been devised by various authors, some of them relying in one form or another on the biased sampling scheme of Rosenbluth and Rosenbluth152 and others on thermodynamic integration methods.8"13 The Rosenbluth-Rosenbluth method was devised originally as a sampling scheme for generating configurations of SAWs on a lattice (see Chapter 2) that avoids the "attrition problem" (i.e., the loss of chain configurations that have to be abandoned because they are overlapping). In this scheme one grows the SAW step by step and checks at each step which sites are available for the next step without violating the SAW constraint. One of these steps is then selected at random. Since relative to the simple sampling of SAWs this method creates a bias,2'153 one has to keep track of the probability of each configuration relative to the unbiased simple sampling, and weigh the generated chain configurations with this probability accordingly. An approximate generalization of this method to multichain systems due to Meirovitch is called "scanning future steps".154'155 Suppose we wish to put Jf chains of N monomers each on a simple cubic lattice of L3 sites. A starting point for the first polymer is selected out of the L3 lattice sites with probability L~3 and occupied by a monomer. The first chain is then grown by a method where one scans b future steps: once the first k monomers have been placed, one counts for each of the six neighbors of the last site the allowed continuations consisting of b further steps (for the monomers k + 1 to k + b) which start at this last site. The probability for selecting one of the six neighbors is chosen proportional to the number of allowed continuations starting at this site. Then the (k+ l) th monomer is placed on the selected site, and so on. In this way one has to place N monomers for the first polymer. If at any step no continuation is possible, the construction is abandoned and one starts a new polymer from a new starting point. Once the first polymer is generated on the lattice, a starting point for the second polymer is selected out of the remaining L3 — N sites with equal probability, and the further N - 1 monomers of the second polymer are placed on the lattice according to the same method as described above. The excluded volume interaction is taken into account with respect to the first chain and the already grown parts of the second chain. This procedure is continued until the desired number of chains on the lattice has been reached. The fraction of successful construction attempts is not an exponentially decreasing function of the number of chains Jf, but stays approximately constant at unity until a critical value that depends on N and Z>.154~156 The larger N and/or jV is, the larger one should use b; however, since the
S E L E C T E D ISSUES ON C O M P U T A T I O N A L T E C H N I Q U E S 31
number of future steps that need to be scanned increases with b, in practice one is again limited to rather small N. Since one knows at each step of the construction of a configuration the probability for selecting a lattice site for the next monomer, one can multiply all these single step probabilities in order to obtain the probability Pv of constructing the multiple chain configuration v. Then the partition function Z is estimated from a sampling of the inverse of Pv
From the partition function the free energy Fjf follows and hence all thermodynamic quantities of interest can be estimated (entropy, chemical potential, osmotic pressure...). Ottinger156 applied this technique to test the osmotic equation of state for dilute and semidilute polymer solutions for N < 60. Extension of this technique to off-lattice systems has also been made.157'158 A variant of the Rosenbluth-Rosenbluth method tailored to overcome the test chain insertion problem in the Widom method143 (Eq. [1.8]) has been developed by Frenkel et a/.159"163 and is known as configurational bias Monte Carlo (CBMC). They rewrite eq. (1.18), using the fact that U = ^2jLi Uj,jf+i> the energy of a test chain of length N inserted into a system of J\f other chains, can be written as a sum of energies Uj^+\ of the individual beads,
Equation (1.20) suggests inserting the test chain bead by bead, and to overcome the sampling problems created by the relatively small probability of randomly inserting a test chain, without overlap, in a frozen snapshot of the system at liquid-like densities. Frenkel et a/.159"163 use a biased insertion procedure which favors low energy conformations of the inserted chain. The first bead is inserted at random and the interaction energy of this bead with the rest of the system (U\tjy+\) tabulated. Then k(\ < k < oc) trial positions are generated for the next bead, obeying any geometric constraints imposed by chain architecture. The energy of each of these trial positions (t4,/r+i) K calculated, and one position (I) is randomly chosen according to a weight Wf,
Subsequent beads of the test chain are grown similarly until one arrives at the desired chain length.
32
INTRODUCTION
One now has to correctly weigh the states generated by this biased insertion procedure when one calculates the chemical potential from eq. (1.20): since we generated states of the canonic ensemble modified with a weighting function, w, we have to correct for this weighting function as follows142'163
where (/~o) represents the desired average of an observable/in the canonic ensemble, and { . . . ) in the weighted ensemble. Applying this to eq. (1.20) yields
noting that no bias needs to be corrected for the first segment. Substituting eq. (1.21) in eq. (1.23) finally yields
It is important to emphasize the distinction between the CBMC method 159-163 ancj jj^ originai Rosenbluth scheme.152 As is well known,153 the latter generates an unrepresentative sample of all polymer conformations, i.e., the probability that a particular conformation is generated is not proportional to the Boltzmann weight of that conformation, and thus one has to correct for the difference in weights and thus arrives at a biased sampling scheme which has problems for large N.153 In the CBMC scheme, on the other hand, the Rosenbluth weight is used to bias the acceptance of trial conformations that are generated with the Rosenbluth scheme. Therefore all conformations occur with their correct Boltzmann weight. This is achieved by computing the Rosenbluth weights wiriai and woki of the trial conformation and of the old conformation (in the trial conformation one may regrow an entire polymer molecule or only a part thereof). Finally the trial move is accepted only with a probability min{wtriai/w0id, !}• As explained by Frenkel,163 this method is also readily applied to off-lattice chains.
S E L E C T E D ISSUES ON C O M P U T A T I O N A L T E C H N I Q U E S 33
At this point, we note that the chemical potential defined from a stepwise insertion procedure as described above can also be written as
Hr(j) being the incremental chemical potential to add a bead to a chain of lengthy— 1. Equation (1.25) is the basis of the chain increment method of Kumar et a/.142'164"167 One now can prove166 an analog of the Widom formula, eq. (1.18), for /ir(/),
where the ensemble that is considered comprises Jf — 1 chains of length N and one chain of length j— 1, with 1 )} to find the excess chemical potential of the polymers (relative to an ideal noninteracting polymer gas):
It was found useful to carry out the integration in eq. (1.27) by performing simulations at about nine distinct values of A, which are used as input into a multihistogram analysis which yields a very good estimate of (A/o(A,(/>))
34
INTRODUCTION
over the whole range of the auxiliary parameter A.168 It was found that this method works very well even for parameters such as N = 80, 0 = 0.5, where the insertion probability that one would have to sample with the Widom method143 would be as small as 10~76. For long chains the applicability of this method is only limited by the requirement that one must have a means of producing a sufficient number of equilibrated and statistically independent configurations in which the ghost chain is immersed to measure the overlap.168
1.4.2 Calculation of pressure in dynamic Monte Carlo methods If a polymer solution is modeled by an assembly of self-avoiding walks on a lattice, a basic physical quantity is the osmotic pressure II. Carrying out a simulation with a fixed number Jf of chains of length TV at a lattice of volume V with one of the dynamic algorithms described in Section 1.2.2, the osmotic pressure is not straightforward to sample. If one had methods that yielded the excess chemical potential A/i and the Helmholtz free energy Fjf, one would find II from the thermodynamic relation
Noting that A/it = Fjf+\ - Fjf (eq. [1.18]) and remembering Fjf = -k^TlnZ(^V, N, V) where Z ( J f , N, V) is the partition function of yT chains of length N in the volume V, it is convenient to relate the insertion probability p(«V, N, V] to a ratio of partition functions,
This quantity describes the probability that a randomly chosen A^-mer, placed at random into a randomly chosen configuration of ^VN-mers on a lattice of volume V, does not overlap any of the Ji~ chains. From eqs (1.18) and (1.29) one derives the relation for the excess chemical potential in terms of this insertion probability,
which can be used to derive eq. (1.27). Since
eq. (1.28) can be rewritten as147
S E L E C T E D ISSUES ON C O M P U T A T I O N A L T E C H N I Q U E S 35
In the thermodynamic limit, the summation over the number of chains can be replaced by a thermodynamic integration over the volume fraction of occupied sites (
no
This result shows that the osmotic pressure can be obtained from a thermodynamic integration if the insertion probability p(4>', N) is sampled over a range of values from ' = 0 to ' = (/>. This method has been applied in conjunction with some of the methods of the previous subsection where the estimation of the chemical potential via the insertion probability was discussed.147'168 An interesting alternative method169'170 relates the pressure of the system to the segment density at a repulsive wall. While usually in simulations one considers a J-dimensional cubic box with all linear dimensions equal to L and periodic boundary conditions, in this method one applies a lattice of length L in d — 1 dimensions and of length H in the remaining direction, with which one associates the coordinate x. There is an infinite repulsive potential at x = 0 and x = H+ 1, while in the other directions periodic boundary conditions apply. The partition function of ^VN-mers on the lattice then is Z(^V,N,L,H) = (J^!)"1 5>xp(-{7/fcB^), where the sum runs over all configurations on the lattice, and the potential U incorporates restrictions which define then chain structure, prohibit overlaps, etc. While for a model in continuous space the pressure is
the lattice analog for this expression is The difference in free energies required here is calculated by introducing a parameter A. 0 < A < 1, which enters as a statistical weight for each monomer in the plane x — H: it may be viewed as being due to an additional finite repulsive potential next to the wall. Denoting the number of occupied sites
36
INTRODUCTION
in the plane x = H as NH, the statistical weight factor due to this auxiliary potential is A^", and hence the partition function becomes
Note that Z(^,N,L,H, 1) = Z(^,N,L,H) and that Z(^,N,L,H,0) = Z(^V,N,L,H - 1), since for A = 0 there are no monomers allowed in the plane x = H; effectively the repulsive wall now is at x = H rather than at x = H+ 1. This yields
Thus one must carry out simulations for several values of A to sample (NH)X, the average number of occupied sites in the plane x = H, in order to perform the above integration numerically.169'170 We now describe, as an example, a few applications of these methods. Figure 1.11 compares simulation results170 for the compressibility factor with predictions of various equations of state, namely of Flory104 of the Flory-Huggins theory103 (q is the coordination number of the lattice)
and of the Bawendi-Freed theory171
It is seen that the Flory approximation is inaccurate, while both other approximations describe the equation of state well at high volume fractions . At small volume fractions, however, neither of these approximations is very accurate, as expected, since in the dilute and semidilute concentration regime a scaling description16'20 of the equation of state is needed. While Fig. 1.11 refers to the simplest lattice model where polymers are described as SAWs (Fig. 1.4), the above techniques are straightforwardly generalixed to more sophisticated lattice models such as the bond fluctuation model (Fig. 1.12).166'172 It is seen that the repulsive wall method and the
S E L E C T E D ISSUES ON C O M P U T A T I O N A L T E C H N I Q U E S 37
Fig. 1.11 Compressibility factor z plotted vs. volume fraction, for self- and mutually-avoiding walks on the simple cubic lattice, and two chain lengths: N = 20 (filled symbols) or N = 40 (open symbols), respectively. The Flory theory104 is shown as a dash-dotted curve, FloryHuggins theory103 as broken curve, and the Bawendi-Freed theory171 as full curve. Circles represent data obtained from the repulsive wall method, while squares or diamonds are obtained from the test-chain insertion method. (From Hertanto and Dickman.170)
insertion method, where one integrates over the strength of excluded volume interaction with the inserted ghost chain168 are in reasonable agreement. In off-lattice simulations in the NVT ensemble the (excess) pressure A/> is usually calculated from the Virial theorem173"175
Again the kinetic energy term p^n = Jik-^T/V where Jf is the number of atoms per volume V in the system, is omitted throughout, and the
38
INTRODUCTION
Fig. 1.12 Osmotic pressure IlV/k^T plotted vs. volume fraction , for the athermal bond fluctuation model on the simple cubic lattice, N = 20. Open squares are obtained by Deutsch and Dickman172 with the repulsive wall method; full squares are based on thermodynamic integration over a variable excluded volume interaction between the inserted "ghost chain" and the other chains.16 Curve shows the pressure according to the "Generalized Flory" equation of state of Ref. 172, U((/>,N)/kj,T= /N+ (l/JV)[v(AO/v(l)][n(0, l)/k9T- ], where v(N) is the exclusion volume of an N-mer. (From Milller and Paul. 68)
summations /, j run over all effective monomers in the system (we use a convention where all pairs are counted twice), U being the total potential energy. One may split eq. (1.42) into three parts: a "covalent" part due to (harmonic) interactions along the chains, an intra-chain part due to nonbonded interactions, and the inter-chain contribution
and
Of course, this separation does not imply that the springs 01 me oeaaspring model must be harmonic; it works for anharmonic forces along the chain as well.
FINAL REMARKS
39
Gao and Weiner174'175 call this pressure contribution A/? due to monomers of the polymers the "atomic pressure" and suggest that it is this quantity that one should consider in the polymer melt. They suggest that at the 6-temperature the covalent part and the nonbonded intrachain part of A/? should cancel, and then the atomic pressure would reduce simply to the osmotic pressure of a polymer solution. Milchev and Binder176a attempted to check this, but it would be interesting to clarify this problem by a comparative study of several other models. A potentially very useful method to obtain entropy, pressure and chemical potential of many-chain systems is the scanning method of Meirovitch.176b Lack of space prevents us from discussing it here. 1.5 Final remarks
The field of computer simulation in polymer science is a very active area of research and many developments of simulation methodology are either very recent or even still under study: this will become even more evident when the reader proceeds to the later chapters in this book. But although applications to many problems in polymer physics have been started just a few years ago—such as large-scale simulations of polymer networks, polymer electrolyte solutions, polymer brushes under various solvent conditions, block copolymer mesophase ordering, and so on—even these very first attempts to simulate complex polymeric materials have already been very useful and given a lot of insight. The main direction of research has not been directed towards the prediction of materials parameters for specific polymers—as discussed in Section 1.1 of the present chapter, such a task is difficult and to a large extent not yet feasible with controlled errors—but towards the test of general concepts (such as various "scaling" ideas developed for the various systems of interest) as well as of specific theories. A huge advantage of the simulations is that one can adjust the model that is simulated very closely to the model that the theory considers: e.g., the Flory-Huggins theory of polymer blend thermodynamics uses a very simple lattice model and then the simulations can provide a stringent test by studying exactly that lattice model (see Chapter 7). On the other hand, the polymer reference interaction site model (PRISM) theory of polymer melts considers idealized bead-spring type off-lattice models of polymer chains, and thus is tested most stringently by a comparison to corresponding molecular dynamics simulations.177 As will be described in later chapters, such comparisons have indeed been very illuminating. At this stage, the comparison between simulation and experiment is somewhat more restricted: either one restricts attention to very short chains of simple enough polymers to allow the treatment of a model including detailed chemistry (Chapters 5, 8) or one has to focus on universal properties. Then a nontrivial comparison between simulation and experiment is
40
INTRODUCTION
still possible, if one compares suitable dimensionless quantities. As an example (more details on this problem will be found in Chapter 4) consider the chain-length-dependence of the self-diffusion coefficient of polymer melts: for short chains one expects that the Rouse model16' 17'79 holds, i.e., the selfdiffusion constant DN varies inversely with chain length, £>ROUse ROUSe = lim(jVDjv)} versus N/Ne (see Fig. 1.13).127 It is seen then that both N3ata from MD simulation,85 MC simulations127 and experiment178 superpose on a common curve. The entanglement chain length Ne has been estimated independently85'127'178 and thus the comparison in Fig. 1.13 does not involve any adjustable parameter whatsoever! The agreement seen in Fig. 1.13 hence is significant and a relevant test of the reptation ideas is indeed provided by these simulations85'127, as will be discussed in more detail in Chapter 4. On the other hand, the very interesting question of how a parameter such as Ne is related to the detailed chemical structure of polymers escapes the tractability of simulational approaches so far.
Fig. 1.13 Log-log plot of the self-diffusion constant D of polymer melts vs. chain length. D is normalized by the diffusion constant of the Rouse limit, DRouse, which is reached for short chain lengths. N is normalized by JVe. Experimental data for polyethylene (PE)178 and MD results85 are included. (From Paul et a/.127)
Acknowledgments
In this chapter research work performed in collaboration with J. Baschnagel, H.-P. Deutsch, I. Gerroff, D. W. Heermann, K. Kremer, A. Milchev, W. Paul, and K. Qin was used to illustrate some of the main
REFERENCES
41
points. It is a pleasure to thank them for a pleasant and fruitful collaboration. The author is also greatly indebted to J. Clarke, R. Dickman, and M. Miiller for being allowed to show some of their recent research results (Figs 1.9(c), 1.11, 1.12). It is also a pleasure to thank K. Kremer and R. Dickman for their useful comments on this manuscript. References 1. A. Baumgartner, in Applications of the Monte Carlo Method in Statistical Physics, edited by K. Binder (Springer, Berlin, 1984), Ch. 5. 2. K. Kremer and K. Binder, Computer Repts 7, 259 (1988). 3. R. J. Roe (ed.) Computer Simulations of Polymers (Prentice Hall, Englewood Cliffs, NJ, 1991). 4. J. Bicerano (ed.) Computational Modelling of Polymers (M. Dekker, New York, 1992). 5. A. Baumgartner, in Monte Carlo Methods in Condensed Matter Physics, edited by K. Binder (Springer, Berlin, 1992), Ch. 9. 6. E. A. Colbourn (ed.) Computer Simulation of Polymers (Longman, Harlow, 1993). 7. K. Kremer, in Computer Simulation in Chemical Physics, edited by M. P. Allen and D. J. Tildesley (Kluwer Academic Publishers, Dordrecht, 1993). 8. M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids (Clarendon Press, Oxford, 1987). 9. G. Ciccotti and W. G. Hoover (eds) Molecular Dynamics of Statistical Mechanical Systems (North-Holland, Amsterdam, 1986). 10. D. W. Heermann, Introduction to Computer Simulation Methods in Theoretical Physics (Springer, Berlin, 1986). 11. K. Binder and D. W. Heermann, Monte Carlo Simulation in Statistical Physics: an Introduction (Springer, Berlin, 1988). 12. K. Binder (ed.) Monte Carlo Methods in Condensed Matter Physics (Springer, Berlin, 1992). 13. M. P. Allen and D. J. Tildesley, Computer Simulation in Chemical Physics (Kluwer Academic Publishers, Dordrecht, 1993). 14. K. Binder, Makromol. Chem., Macromol. Symp. 50, 1 (1991). 15. P. J. Flory, Statistical Mechanics of Chain Molecules (Interscience, New York, 1969). 16. P. G. de Gennes, Scaling Concepts in Polymer Physics (Cornell University Press, Ithaca, NY, 1979). 17. M. Doi and S. F. Edwards, Theory of Polymer Dynamics (Clarendon Press, Oxford, 1986). 18. A. Halperin, M. Tirrell, and T. P. Lodge, Adv. Polym. Sci. 100, 31 (1991). 19. P. J. Flory, Principles of Polymer Chemistry (Cornell University Press, Ithaca, 1953). 20. J. des Cloizeaux and G. Jannink, Polymers in Solution: their Modelling and Structure (Oxford University Press, Oxford, 1990). 21. K. Binder, Adv. Polym. Sci. 112, 181 (1994). 22. K. Binder, /. Chem. Phys. 79, 6387 (1983).
42
INTRODUCTION
23. H. P. Deutsch and K. Binder, /. Phys. (France) II 3, 1049 (1993). 24. J. C. Le Guillou and J. Zinn-Justin, Phys. Rev. B21, 3976 (1980). 25. G. Meier, D. Schwahn, K. Mortensen, and S. Janssen, Europhys.Lett. 22, 577 (1993). 26. F. S. Bates and P. Wiltzius, J. Chem. Phys. 91, 3258 (1989). 27. T. Hashimoto, in Materials Science and Technology, Vol. 12: Structure and Properties of Polymers, edited by E. L. Thomas (VCH, Weinheim, 1993), p. 251. 28. F. S. Bates and G. H. Fredrickson, Ann. Rev. Phys. Chem. 41, 525 (1990). 29. P. G. de Gennes, P. Pincus, and R. Velasco, J. Phys. (Paris) 37, 1461 (1976). 30. J. Skolnick and M. Fixman, Macromolecules 10, 944 (1977). 31. T. Odijk, J. Polym. ScL, Polym. Phys. Ed. 15, 477 (1977); Polymer 19, 989 (1978). 32. J. Hayter, G. Jannink, F. Brochard-Wyart, and P. G. de Gennes, /. Phys. (Paris) Lett. 41, 451 (1980). 33. P. Y. Lai and K. Binder, J. Chem. Phys. 98, 2366 (1993), and references therein. 34. J. P. Ryckaert and A. Bellemans, Chem. Phys. Lett. 30, 123 (1975). 35. J. P. Ryckaert and A. Bellemans, Discuss. Faraday. Soc. 66, 95 (1978). 36. J. H. R. Clarke and D. Brown, Molec. Phys. 58, 815 (1986). 37. J. H. R. Clarke and D. Brown, Molec. Simul. 3, 27 (1989). 38. D. Brown, J. H. R. Clarke, M. Okuda, and T. Yamazaki, /. Chem. Phys. 100, 1684 (1994). 39. D. J. Rigby and R. J. Roe, J. Chem. Phys. 87, 7285 (1987). 40. D. J. Rigby and R. J. Roe, J. Chem. Phys. 88, 5280 (1988). 41. D. J. Rigby and R. J. Roe, Macromolecules 22, 2259 (1989); 23, 5312 (1990). 42. H. Takeuchi and R. J. Roe, /. Chem. Phys. 94, 7446, 7458 (1991); R. J. Roe, D. Rigby, H. Furuya, and T. Takeuchi, Comput. Polym. Sci. 2, 32 (1992). 43. J. Baschnagel, K. Qin, W. Paul, and K. Binder, Macromolecules 25, 3117 (1992). 44. A. Sariban, J. Brickmann, J. van Ruiten, and R. J. Meier, Macromolecules 25, 5950 (1992). 45. J. Baschnagel, K. Binder, W. Paul et al., /. Chem. Phys. 95, 6014 (1991). 46. B. Smit and D. Frenkel, /. Chem. Phys. 94, 5663 (1991). 47. S. Karaborni, S. Toxvaerd, and O. H. Olsen, /. Phys. Chem. 96, 4965 (1992). 48. D. Y. Yoon, G. D. Smith, and T. Matsuda, J. Chem. Phys. 98, 10037 (1993); G. D. Smith, R. L. Jaffe, and D. Y. Yoon, Macromolecules 26, 293 (1993). 49. B. Smit, S. Karaborni, and J. Siepmann, Macromol.Symp. 81, 343 (1994) (paper presented at the First International Conference on the Statistical Mechanics of Polymer Systems, Theory and Simulations, Mainz, Germany Oct 4-6, 1993). 50. D. N. Theodorou and U. W. Suter, Macromolecules 18, 1467 (1985); 19, 139 (1986); ibid 19, 379 (1986). 51. K. F. Mansfield and D. N. Theodorou, in Computer Simulations of Polymers (Prentice Hall, Englewood Cliffs, NJ, 1991), p. 122; Macromolecules 24, 6283 (1991). 52. M. F. Sylvester, S. Yip, and A. S. Argon, in Computer Simulations of Polymers (Prentice Hall, Englewood Cliffs, NJ, 1991), p. 105.
REFERENCES
43
53. G. C. Rutledge and U. W. Suter, Polymer 32, 2179 (1991); Macromolecules 24, 1921 (1991). 54. M. Hutnik, F. T. Gentile, P. J. Ludovice, U. W. Suter, and A. S. Argon, Macromolecules 24, 5962 (1991); M. Hutnik, A. S. Argon, and U. W. Suter, Macromolecules 24, 5956 (1991). 55. P. J. Ludovice and U. W. Suter, in Computational Modelling of Polymers (M. Dekker, New York, 1992), p. 401. 56. D. B. Adolf and M. D. Ediger, in Computer Simulations of Polymers (Prentice Hall, Englewood Cliffs, NJ, 1991), p. 154. 57. R. H. Boyd and K. Pant, in Computer Simulations of Polymers (Prentice Hall, Englewood Cliffs, NJ, 1991), p. 94. 58. B. G. Sumpter, D. W. Noid, B. Wunderlich, and S. Z. D. Cheng, in Computer Simulations of Polymers (Prentice Hall, Englewood Cliffs, NJ, 1991), p. 311. 59. J. Baschnagel, K. Binder, and H. P. Wittmann, /. Phys. Condens. Matter 5, 1597 (1993). 60. J. Baschnagel and K. Binder, Physica A 204, 47 (1994). 61. A. Sariban and K. Binder, /. Chem. Phys. 86, 5859 (1987). 62. A. Sariban and K. Binder, Macromolecules 21, 711 (1988). 63. H. P. Deutsch and K. Binder, Macromolecules 25, 6214 (1992). 64. H. Snyder, S. Reich, and P. Meakin, Macromolecules 16, 757 (1983). 65. A. Gumming, P. Wiltzius, and S. F. Bates, Phys. Rev. Lett. 65, 863 (1990). 66. J. Jackie, Reports Progr. Phys. 49, 171 (1986). 67. W. Gotze, in Liquids, Freezing and the Glass Transition, edited by J. P. Hansen, D. Levesque and J. Zinn-Justin (North Holland, Amsterdam, 1990). 68. G. Adam and J. H. Gibbs, J. Chem. Phys. 43, 139 (1965). 69. H. P. Wittmann, K. Kremer, and K. Binder, J. Chem. Phys. 96, 6291 (1992). 70. W. Paul, K. Binder, K. Kremer, and D. W. Heermann, Macromolecules 24, 6332 (1991). 71. W. Paul, AIP Conf. Proc. 256, 145 (1992). 72. W. Paul, K. Binder, J. Batoulis, B. Pittel, and K. H. Sommer, Makromol. Chem., Macromol. Symp. 65, 1 (1993). 73. I. Carmesin and K. Kremer, Macromolecules 21, 2819 (1988). 74. Y. Bar-Yam, Y. Rabin, and M. A. Smith, Macromolecules 25, 2985 (1992). 75. M. A. Smith, Y. Bar-Yam, B. Ostrowsky et al, Comput. Polym. Sci. 2, 165 (1992). 76. I. Gerroff, A. Milchev, K. Binder, and W. Paul, /. Chem. Phys. 98, 6526 (1993). 77. A. Milchev, W. Paul and K. Binder, /. Chem. Phys. 99, 4786 (1993). 78. K. Binder, in Computational Modelling of Polymers (M. Dekker, New York, 1992), p. 221. 79. P. E. Rouse, J. Chem. Phys. 21, 127 (1953). 80. A. Baumgartner and K. Binder, J. Chem. Phys. 75, 2994 (1981). 81. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. N. Teller, and E. Teller, J. Chem. Phys. 21, 1087 (1953). 82. A. Baumgartner, Ann. Rev. Phys. Chem. 35, 419 (1984). 83. G. S. Grest and K. Kremer, Phys. Rev. A33, 3628 (1986).
44
INTRODUCTION
84. B. Diinweg and K. Kremer, Phys. Rev. Lett. 66, 2996 (1991); /. Chem. Phys. 99, 6983 (1993); B. Diinweg, J. Chem. Phys. 99, 6977 (1993). 85. K. Kremer and G. S. Grest, J. Chem. Phys. 92, 5057 (1990). 86. K. Kremer and G. S. Grest, J. Chem. Soc. Faraday Trans. 88, 1707 (1992), and in Computer Simulations of Polymers (Prentice Hall, Englewood Cliffs, NJ, 1991), p. 167. 87. M. Bishop, D. Ceperley, H. L. Frisch, and M. H. Kalos, /. Chem. Phys. 76, 1557 (1982). 88. T. A. Weber, J. Chem. Phys. 69, 2347 (1978); 70, 4277 (1979). 89. T. A. Weber and A. Helfand, /. Chem. Phys. 71, 4760 (1979); 87, 2881 (1983). 90. E. Helfand, Z. Wasserman, and T. Weber, Macromolecules 13, 526 (1980). 91. D. Ceperley, M. H. Kalos, and J. L. Lebowitz, Phys. Rev. Lett. 41, 313 (1978); Macromolecules 14, 1472 (1981). 92. A. A. Darinskii, Yu-Ya Gotlib, A. V. Ljutin, L. I. Khushin, and I. M. Neelov, Polym Sci. (USSR) 32, 2289 (1990). 93. A. A. Darinskii, M. N. Lukjanov, Yu-Ya Gotlib, and I. M. Neelov, /. Phys. Chem. (USSR) 57, 954 (1981). 94. A. A. Darinskii, Yu-Ya Gotlib, A. V. Ljutin, L. I. Khushin, and I. M. Neelov Polym Sci. (USSR) 33, 1211 (1991). 95. R. B. Bird, R. C. Armstrong, and D. Hassager, Dynamics of Polymeric Liquids (J. Wiley, New York, 1971). 96. G. S. Grest, B. Diinweg, and K. Kremer, Comp. Phys. Commun. 55, 269 (1989); R. Everaers and K. Kremer, Comp. Phys. Commun. 81, 19 (1994). 97. C. Pierleoni and J. P. Ryckaert, Phys. Rev. Lett. 66, 2992 (1991); J. Chem. Phys. 96, 8539 (1992). 98. G. S. Grest, K. Kremer, and T. A. Witten, Macromolecules 20, 1376 (1987). 99. G. S. Grest, K. Kremer, S. T. Milner, and T. A. Witten, Macromolecules 22, 1904 (1989). 100. E. R. Duering, K. Kremer, and G. S. Grest, Phys. Rev. Lett. 67, 3531 (1991); Macromolecules 26, 3241 (1993); G. S. Grest, K. Kremer, and E. R. Duering, Physica A194, 330 (1993). 101. G. S. Grest and K. Kremer, /. Phys. (France) 51, 2829 (1990); Macromolecules 23, 4994 (1990). 102. G. S. Grest, K. Kremer, and E. R. Duering, Europhys. Lett. 19, 195 (1992). 103. M. J. Huggins, J. Chem. Phys. 9, 440 (1941). 104. P. J. Flory, J. Chem. Phys. 9, 660 (1941). 105. P. H. Verdier and W. H. Stockmayer, J. Chem. Phys. 36, 227 (1962). 106. P. H. Verdier, J. Chem. Phys. 45, 2122 (1966); 52, 5512 (1970); 59, 6119 (1973). 107. H. J. Hilhorst and J. M. Deutch, /. Chem. Phys. 63, 5153 (1975); H. Boots and J. M. Deutch, 67, 4608 (1977). 108. A. K. Kron, Polym Sci. USSR 7, 1361 (1965); A. K. Kron and O. B. Ptitsyn, Polym Sci. USSR 9, 847 (1967). 109. F. T. Wall and F. Mandel, J. Chem. Phys. 63, 4592 (1975). 110. M. Lai, Molec. Phys. 17, 57 (1969). 111. O. F. Olaj and K. H. Pelinka, Makromol Chem. Ill, 3413 (1976). 112. B. MacDonald, N. Jan, D. L. Hunter, and M. O. Steinitz, J. Phys. A 18, 2627 (1985).
REFERENCES
45
113. N. Madras and A. D. Sokal, /. Stat. Phys. 50, 109 (1988). 114. M. T. Gurler, C. C. Crabb, D. M. Dahlin, and J. Kovac, Macromolecules 16, 389 (1983). 115. J. Skolnick, R. Yaris, and A. Kolinski, J. Chem. Phys. 88, 1407 (1988). 116. N. Madras, A. Orlitsky, and L. A. Shepp, /. Stat. Phys. 58, 159 (1990). 117. N. Madras and A. D. Sokal, /. Stat. Phys. 47, 573 (1987). 118. A. Baumgartner, J.Phys. A 17, L971 (1984). 119. A. Baumgartner and D. W. Heermann, Polymer 27, 1777 (1986). 120. A. Beretti and A. D. Sokal, /. Stat. Phys. 40, 483 (1985). 121. T. Pakula, Macromolecules 20, 679 (1987); T. Pakula and S. Geyler, Macromolecules 20, 2909 (1987). 122. S. Geyler, T. Pakula, and J. Reiter, J. Chem. Phys. 92, 2676 (1990). 123. J. Reiter, T. Edling, and T. Pakula, /. Chem. Phys. 93, 837 (1990). 124. P. Cifra, F. E. Karasz, and W. J. MacKnight, Macromolecules 25,4895 (1992). 125. I. Carmesin and K. Kremer, /. Phys. (France) 51, 915 (1990). 126. H.-P. Wittmann and K. Kremer, Comp. Phys. Commun. 61, 309 (1990); Comp. Phys. Commun. 71, 343 (1992), erratum. 127. W. Paul, K. Binder, D. W. Heermann, and K. Kremer, J. Phys. II (France) 1, 37 (1991). 128. W. Paul, K. Binder, D. W. Heermann, and K. Kremer, /. Chem. Phys. 95, 7726 (1991). 129. H. P. Deutsch and K. Binder, /. Chem. Phys. 94, 2294 (1991). 130. M. Schulz and J.-U. Sommer, J. Chem. Phys. 96, 7102 (1992); M. Schulz and K. Binder, J. Chem. Phys. 98, 655 (1993). 131. J. Batoulis, N. Pistoor, K. Kremer, and H. L. Frisch, Electrophoresis 10, 442 (1989). 132. P. G. de Gennes, /. Chem. Phys. 55, 572 (1971). 133. M. Murat and T. A. Witten, Macromolecules 23, 520 (1990); A. R. C. BaljonHaakman and T. A. Witten, Macromolecules 25, 2969 (1992). 134. J. Batoulis and K. Kremer, Europhys. Lett. 7, 683 (1988); Macromolecules 22, 4277 (1989). 135. K. Ohno and K. Binder, /. Stat. Phys. 64, 781 (1991); K. Ohno, X. Hu, and Y. Kawazoe, in Computer-Aided Innovation of New Materials II, edited by M. Doyama, J. Kihara, M. Tanaka, and R. Yamamoto (Elsevier, Amsterdam, 1993), p. 315. 136. N. Pistoor and W. Paul, Macromolecules 27, 1249 (1994). 137. P.-Y. Lai and K. Binder, J. Chem. Phys. 95, 9288 (1991); P.-Y. Lai, /. Chem. Phys. 98, 669 (1993). 138. P.-Y. Lai and K. Binder, /. Chem. Phys. 97, 586 (1992). 139. F. Haas, P.-Y. Lai, and K. Binder, Makromol. Chem., Theory & Simul. 2, 889 (1993). 140. A. Yethiraj and R. Dickman, J. Chem. Phys. 97, 4468 (1992). 141. B. Zimm, /. Chem. Phys. 24, 269 (1956). 142. S. Kumar, in Computer Simulation of Polymers, edited by E. A. Colbourn (Longman, Harlow, U.K., 1993) Chapter 8. 143. B. Widom, J. Chem. Phys. 39, 2808 (1962). 144. K. S. Shing and K. E. Gubbins, Mol. Phys. 43, 717 (1981); 46, 1109 (1982).
46
INTRODUCTION
145. J. G. Powles, W. A. B. Evans, and N. Quirke, Mol. Phys. 46, 1347 (1982). 146. H. Okamoto, /. Chem. Phys. 64, 2868 (1976); 79, 3976 (1983); 83, 2587 (1986). 147. H. Okamoto, K. Itoh, and T. Araki, J. Chem. Phys. 78, 985 (1983); R. Dickman and C. K. Hall, /. Chem. Phys. 85, 3023 (1986). 148. R. Dickman and C. K. Hall, /. Chem. Phys. 89, 3168 (1988). 149. K. G. Honnell, R. Dickman, and C. K. Hall, J. Chem. Phys. 87, 664 (1987). 150. K. G. Honnell and C. K. Hall, /. Chem. Phys. 90, 1841 (1987). 151. C. A. Croxton, Phys. Lett. A70, 441 (1979). 152. M. N. Rosenbluth and A. W. Rosenbluth, /. Chem. Phys. 23, 356 (1955). 153. I. Batoulis and K. Kremer, /. Phys. All, 127 (1988). 154. H. Meirovitch, /. Chem. Phys. 79, 502 (1983). 155. H. Meirovitch, Macromolecules 16, 249 (1983); Macromolecules 16, 1628 (1983). 156. H. C. Ottinger, Macromolecules 18, 93 (1985); 18, 1348 (1985). 157. H. Meirovitch, Phys. Rev. A32, 3699 (1985). 158. J. Harris and S. A. Rice, J. Chem. Phys. 88, 1292 (1988). 159. G. C. A. Mooij and D. Frenkel, Mol. Phys. 74, 41 (1991); J. I. Siepmann, Mol. Phys. 70, 1145(1990). 160. D. Frenkel and B. Smit, Mol. Phys. 75, 983 (1992). 161. D. Frenkel, G. C. A. M. Mooij, and B. Smit, J. Phys. Condens. Matter 4, 3053 (1992). 162. J. J. de Pablo, M. Laso, and U. W. Suter, /. Chem. Phys. 96, 6157 (1992). 163. D. Frenkel, in Computer Simulation in Chemical Physics (Kluwer Academic Publishers, Dordrecht, 1993), p. 93. 164. S. K. Kumar, I. Szleifer and A. Z. Panagiotopoulos, Phys. Rev. Lett. 66, 2935 (1991). 165. S. K. Kumar, J. Chem. Phys. 96, 1490 (1992). 166. S. K. Kumar, I. Szleifer, and A. Z. Panagiotopoulos, Phys. Rev. Lett. 68, 3658 (1992). 167. I. Szleifer and A. Z. Panagiotopoulos, /. Chem. Phys. 97, 6666 (1992). 168. M. Miiller and W. Paul, J. Chem. Phys. 100, 719 (1994). 169. R. Dickman, /. Chem. Phys. 86, 2246 (1987). 170. A. Hertanto and R. Dickman, J. Chem. Phys. 89, 7577 (1988). 171. M. G. Bawendi and K. F. Freed, J. Chem. Phys. 88, 2741 (1988). 172. H. P. Deutsch and R. Dickman, J. Chem. Phys. 93, 8983 (1990). 173. C. G. Cray and K. I. Gubbins, Theory of Molecular Fluids (Clarendon Press, Oxford, 1982). 174. J. Gao and J. H. Weiner, J. Chem. Phys. 90, 6749 (1989). 175. J. Gao and J. H. Weiner, /. Chem. Phys. 91, 3168 (1989). 176a. A. Milchev and K. Binder, Macromol. Theory Simul. 3, 915 (1994). 176b. H. Meirovitch, /. Chem. Phys. 97, 5803, 5816 (1992), and references therein. 177. J. G. Curro, K. S. Schweizer, G. S. Grest, and K. Kremer, /. Chem. Phys. 91, 1357 (1989). 178. D. S. Pearson, G. Verstrate, E. von Meerwall, and F. C. Schilling, Macromolecules 20, 1133 (1987).
2
MONTE CARLO METHODS FOR THE SELF-AVOIDING WALK Alan D. Sokal
2.1 Introduction
2.1.1 Why is the SAW a sensible model? The self-avoiding walk (SAW) was first proposed nearly half a century ago as a model of a linear polymer molecule in a good solvent.1'2 At first glance it seems to be a ridiculously crude model, almost a caricature: real polymer molecules live in continuous space and have tetrahedral (109.47°) bond angles, a non-trivial energy surface for the bond rotation angles, and a rather complicated monomer-monomer interaction potential. By contrast, the self-avoiding walk lives on a discrete lattice and has non-tetrahedral bond angles (e.g., 90° and 180° on the simple cubic lattice), an energy independent of the bond rotation angles, and a repulsive hard-core monomer-monomer potential. In spite of these rather extreme simplifications, there is now little doubt that the self-avoiding walk is not merely an excellent but in fact a perfect model for some (but not all!) aspects of the behavior of linear polymers in a good solvent.^ This apparent miracle arises from universality, which plays a central role in the modern theory of critical phenomena.3'4 In brief, critical statistical-mechanical systems are divided into a small number of universality classes, which are typically characterized by spatial dimensionality, symmetries and other rather general properties. In the vicinity of a critical point (and only there), the leading asymptotic behavior is exactly the same (modulo some system-dependent scale factors) for all systems of a given universality class; the details of chemical structure, interaction energies and so forth are completely irrelevant (except for setting the nonuniversal scale factors). Moreover, this universal behavior is given by simple scaling laws, in which the dependent variables are generalized homogeneous functions of the parameters which measure the deviation from criticality.
'More precisely, linear polymers whose backbones consist solely of carbon-carbon single bonds. fHere "good solvent" means "at any temperature strictly above the theta temperature for the given polymer—solvent pair".
48
M O N T E C A R L O M E T H O D S FOR THE SAW
The key question, therefore, is to determine for each physical problem which quantities are universal and which are nonuniversal. To compute the nonuniversal quantities, one employs the traditional methods of theoretical physics and chemistry: semi-realistic models followed by a process of successive refinement. All predictions from such models must be expected to be only approximate, even if the mathematical model is solved exactly, because the mathematical model is itself only a crude approximation to reality. To compute the universal quantities, by contrast, a very different approach is available: one may choose any mathematical model (the simpler the better) belonging to the same universality class as the system under study, and by solving it determine the exact values of universal quantities. Of course, it may not be feasible to solve this mathematical model exactly, so further approximations (or numerical simulations) may be required in practice; but these latter approximations are the only sources of error in the computation of universal quantities. At a subsequent stage it is prudent to test variants and refinements of the original model, but solely for the purpose of determining the boundaries of the universality class: if the refined model belongs to the same universality class as the original model, then the refinement has zero effect on the universal quantities. The behavior of polymer molecules as the chain length tends to infinity is, it turns out, a critical phenomenon in the above sense.5 Thus, it is found empirically—although the existing experimental evidence is admittedly far from perfect6"10—that the mean-square radius of gyration (R^ of a linear polymer molecule consisting of W monomer units has the leading asymptotic behavior as N —> oo, where the critical exponent v w 0.588 is universal, i.e. exactly the same for all polymers, solvents and temperatures (provided only that the temperature is above the theta temperature for the given polymer-solvent pair). The critical amplitude A is nonuniversal, i.e., it depends on the polymer, solvent, and temperature, and this dependence is not expected to be simple. There is therefore good reason to believe that any (real or mathematical) linear polymer chain which exhibits some flexibility and has short-range,* predominantly repulsive^ monomer-monomer interactions lies in the same
*Here I mean that the potential is short-range in physical space. It is of course—and this is a crucial point—long-range along the polymer chain, in the sense that the interaction between two monomers depends only on their positions in physical space and is essentially independent of the locations of those monomers along the chain. tHere "predominantly repulsive" means "repulsive enough so that the temperature is strictly above the thcta temperature for the given polymer-solvent pair".
INTRODUCTION
49
universality class as the self-avoiding walk. This belief should, of course, be checked carefully by both numerical simulations and laboratory experiments; but at present there is, to my knowledge, no credible numerical or experimental evidence that would call it into question. 2.7.2 Numerical methods for the self-avoiding walk Over the decades, the SAW has been studied extensively by a variety of methods. Rigorous methods have thus far yielded only fairly weak results;11 the SAW is, to put it mildly, an extremely difficult mathematical problem. Non-rigorous analytical methods, such as perturbation theory and selfconsistent-field theory, typically break down in precisely the region of interest, namely long chains.12 The exceptions are methods based on the renormalization group (RG),13~15 which have yielded reasonably accurate estimates for critical exponents and for some universal amplitude ratios.16"24 However, the conceptual foundations of the renormalizationgroup methods have not yet been completely elucidated;25'26 and high-precision RG calculations are not always feasible. Thus, considerable work has been devoted to developing numerical methods for the study of long SAWs. These methods fall essentially into two categories: exact enumeration and Monte Carlo. In an exact-enumeration study, one first generates a complete list of all SAWs up to a certain length (usually N w 15—35 steps), keeping track of the properties of interest such as the number of such walks or their squared endto-end distances.27 One then performs an extrapolation to the limit N —»• oo, using techniques such as the ratio method, Fade approximants or differential approximants.28"30 Inherent in any such extrapolation is an assumption about the behavior of the coefficients beyond those actually computed. Sometimes this assumption is fairly explicit; other times it is hidden in the details of the extrapolation method. In either case, the assumptions made have a profound effect on the numerical results obtained.25 For this reason, the claimed error bars in exact-enumeration/extrapolation studies should be viewed with a healthy skepticism. In a Monte Carlo study, by contrast, one aims to probe directly the regime of fairly long SAWs (usually N w 102—105 steps). Complete enumeration is unfeasible, so one generates instead a random sample. The raw data then contain statistical errors, just as in a laboratory experiment. These errors can, however, be estimated—sometimes even a priori (see Section 2.7.3)— and they can in principle be reduced to an arbitrarily low level by the use of sufficient computer time. An extrapolation to the regime of extremely long SAWs is still required, but this extrapolation is much less severe than in the case of exact-enumeration studies, because the point of departure is already much closer to the asymptotic regime.
50
MONTE C A R L O M E T H O D S FOR THE SAW
Monte Carlo studies of the self-avoiding walk go back to the early 1950s,31'32 and indeed these simulations were among the first applications of a new invention—the "high-speed electronic digital computer"—to pure science.* These studies continued throughout the 1960s and 1970s, and benefited from the increasingly powerful computers that became available. However, progress was slowed by the high computational complexity of the algorithms then being employed, which typically required a CPU time of order at least N2+2v = TV*3'2 to produce one "effectively independent" TV-step SAW. This rapid growth with N of the autocorrelation time—called critical slowing-down*—made it difficult in practice to do high-precision simulations with N greater than about 30-100. In the past decade—since 1981 or so—vast progress has been made in the development of new and more efficient algorithms for simulating the selfavoiding walk. These new algorithms reduce the CPU time for generating an "effectively independent" TV-step SAW from ~ TV*3-2 to ~ N**2 or even ~ TV. The latter is quite impressive, and indeed is the best possible order of magnitude, since it takes a time of order TV merely to write down an TV-step walk! As a practical matter, the new algorithms have made possible high-precision simulations at chain lengths TV up to nearly 105.39 The purpose of this chapter is thus to give a comprehensive overview of Monte Carlo methods for the self-avoiding walk, with emphasis on the extraordinarily efficient algorithms developed since 1981.1 shall also discuss briefly some of the physical results which have been obtained from this work. The plan of this chapter is as follows: I begin by presenting background material on the self-avoiding walk (Section 2.2) and on Monte Carlo methods (Section 2.3). In Section 2.4 I discuss static Monte Carlo methods for the generation of SAWs: simple sampling and its variants, inversely restricted sampling (Rosenbluth-Rosenbluth algorithm) and its variants, and dimerization. In Section 2.5 I discuss quasi-static Monte Carlo methods: enrichment and incomplete enumeration (Redner-Reynolds algorithm). In Section 2.6 I discuss dynamic Monte Carlo methods: the methods are classified according to whether they are local or non-local, whether they are TVconserving or TV-changing, and whether they are endpoint-conserving or endpoint-changing. In Section 2.7 I discuss some miscellaneous algorithmic and statistical issues. In Section 2.8 I review some preliminary physical results which have been obtained using these new algorithms. I conclude
'Here "pure" means "not useful in the sense of Hardy": "a science is said to be useful if its development tends to accentuate the existing inequalities in the distribution of wealth, or more directly promotes the destruction of human life" [Ref. 33, p. 120n]. fpor a general introduction to critical slowing-down in Monte Carlo simulations, see Refs 34-37. See also Ref. 38 for a pioneering treatment of critical slowing-down in the context of dynamic critical phenomena.
THE S E L F - A V O I D I N G W A L K (SAW)
51
(Section 2.9) with a brief summary of practical recommendations and a listing of open problems. For previous reviews of Monte Carlo methods for the self-avoiding walk, see Kremer and Binder40 and Madras and Slade (Ref. 11, Chapter 9). 2.2 The self-avoiding walk (SAW)
2.2.7 Background and notation In this section we review briefly the basic facts and conjectures about the SAW that will be used in the remainder of this chapter. A comprehensive survey of the SAW, with emphasis on rigorous mathematical results, can be found in the excellent new book by Madras and Slade.11 Real polymers live in spatial dimension d = 3 (ordinary polymer solutions) or in some cases in d = 2 (polymer monolayers confined to an interface41'42). Nevertheless, it is of great conceptual value to define and study the mathematical models—in particular, the SAW—in a general dimension d. This permits us to distinguish clearly between the general features of polymer behavior (in any dimension) and the special features of polymers in dimension d = 3.* The use of arbitrary dimensionality also makes available to theorists some useful technical tools (e.g., dimensional regularization) and some valuable approximation schemes (e.g., expansion in d = 4 — e).15 So let Jz? be some regular ^-dimensional lattice. Then an N-step self-avoiding waltf (SAW) ,v). However, all probability distributions and all observables that we shall consider are invariant under reversal of orientation (tit = UN-I}- This is necessary if the SAW is to be a sensible model of a real homopolymer molecule, which is of course (neglecting endgroup effects) unoriented.
52
M O N T E C A R L O M E T H O D S FOR THE SAW
First we define the quantities relating to the number (or "entropy") of SAWs: Let CN (resp. CN(X)) be the number of TV-step SAWs on Zrf starting at the origin and ending anywhere (resp. ending at x). Then CN and CN(X) are believed to have the asymptotic behavior
as N —> oo; here // is called the connective constant of the lattice, and 7 ar asing are critical exponents. The connective constant is definitely lattic dependent, while the critical exponents are believed to be universal amor lattices of a given dimension d. (For rigorous results concerning the asymj totic behavior of CN and CN(X), see Refs 11, 48-51.) Next we define several measures of the size of an TV-step SAW: • The squared end-to-end distance • The squared radius of gyration
• The mean-square distance of a monomer from the endpoints
We then consider the mean values (R^)N, (&£)N and (Rl,)N in tne probability distribution which gives equal weight to each TV-step SAW. Very little has been proven rigorously about these mean values, but they are believed to have the asymptotic behavior as TV —* oo, where v is another (universal) critical exponent. Moreover, the amplitude ratios
THE S E L F - A V O I D I N G W A L K (SAW)
53
are expected to approach universal values in the limit N —> oo.*'t Finally, let cN{^2 be the number of pairs (u/1), a/2)) such that u/1) is an TVi-step SAW starting at the origin, a/2) is an A^-step SAW starting anywhere, and a/1) and w^ have at least one point in common (i.e., u/1) n w(2) ^ 0). Then it is believed that as NI , N2 —> oo, where A4 is yet another (universal) critical exponent and g is a (universal) scaling function. The quantity CffltN2 is closely related to the second virial coefficient. To see this, consider a rather general theory in which "molecules" of various types interact. Let the molecules of type z have a set Sj of "internal states", so that the complete state of such a molecule is given by a pair (x, s) where x e /rf is its position and s e Sf is its internal state. Let us assign Boltzmann weights (or "fugacities") Wt(s) [s e 51,] to the internal states, normalized so that Y^ses- Wi(s) = 1; and let us assign an interaction energy irij((x,s),(x',s')} [x,x'eZd,seSi,s'&Sj] to a molecule of type / at (x, s) interacting with one of type j at (x', s'). Then the second virial coefficient between a molecule of type / and one of type j is
In the SAW case, the types are the different lengths N, the internal states are the conformations w e S^n starting at the origin, the Boltzmann weights are WN(U) = I/CAT for each w e £fN, and the interaction energies are hard-core repulsions
*For a general discussion of universal amplitude ratios in the theory of critical phenomena, see Ref. 52. tVery recently, Kara and Slade48'49 have proven that the SAW in dimension d > 5 converges weakly to Brownian motion when N —> oo with lengths rescaled by C7V1/2 for a suitable (nonuniversal) constant C. It follows from this that eq. (2.7) holds with v = 5, and also that eqs (2.8)/(2.9) have the limiting values A^ = \, B^ = \. Earlier, Slade53"55 had proven these results for sufficiently high dimension d. See also Ref. 11.
54
M O N T E C A R L O M E T H O D S FOR THE SAW
It follows immediately that
The second virial coefficient B^'N2' is a measure of the "excluded volume" between a pair of SAWs. It is useful to define a dimemionless quantity by normalizing B% by some measure of the "size" of these SAWs. Theorists prefer (R^} as the measure of size, while experimentalists prefer {R^} since it can be measured by light scattering. We follow the experimentalists and define the mterpenetration ratio
(for simplicity we consider only N\=NI= N). The numerical prefactor is a convention that arose historically for reasons not worth explaining here. Crudely speaking, * measures the degree of "hardness" of a SAW in its interactions with other SAWs.* tyN is expected to approach a universal value ** in the limit N —> oo. A deep question is whether ** is nonzero (this is called hyper scaling). It is now known that hyperscaling fails for SAWs in dimension d > 4.11-48'49 it is believed that hyperscaling holds for SAWs in dimension d < 4, but the theoretical justification of this fact is a key unsolved problem in the theory of critical phenomena (see e.g., Ref. 39).t Higher virial coefficients can be defined analogously, but the details will not be needed here. Remark The critical exponents defined here for the SAW are precise analogues of the critical exponents as conventionally defined for ferromagnetic spin systems.57'58 Indeed, the generating functions of the SAW are equal to the correlation functions of the w-vector spin model analytically * A useful standard of comparison is the hard sphere of constant density:
'/\ very oeauuiui neunsiic argument concerning nyperscanng 101 :v\ws was given oy ues Cloizeaux.56 Note first from eq. (2.14b) that \P measures, roughly speaking, the probability of intersection of two independent SAWs that start a distance of order {R2,}1/2 ~ N" apart. Now, by eq. (2.7), we can interpret a long SAW as an object with "fractal dimension" 1/v. Two independent such objects will "generically" intersect if and only if the sum of their fractal dimensions is at least as large as the dimension of the ambient space. So we expect \&* to be nonzero if and only if \/v + \jv > d, i.e., dv < 1. This occurs for d < 4. (For d — 4 we believe that dv = "2 + logs", and thus expect a logarithmic violation of hyperscaling.)
THE S E L F - A V O I D I N G W A L K (SAW)
55
continued to n = o.u'59~62 This "polymer-magnet correspondence"* is very useful in polymer theory; but we shall not need it in this chapter.
2.2.2 The ensembles Different aspects of the SAW can be probed in four different ensembles^: • • • •
Fixed-length, fixed-endpoint ensemble (fixed N, fixed x) Fixed-length, free-endpoint ensemble (fixed N, variable x) Variable-length, fixed-endpoint ensemble (variable N, fixed x) Variable-length, free-endpoint ensemble (variable N, variable x)
The fixed-length ensembles are best suited for studying the critical exponents v and 2A4 — 7, while the variable-length ensembles are best suited for studying the connective constant \JL and the critical exponents asing (fixedendpoint) or 7 (free-endpoint). Physically, the free-endpoint ensembles correspond to linear polymers, while the fixed-endpoint ensembles with \x = 1 correspond to ring polymers. All these ensembles give equal weight to all walks of a given length; but the variable-length ensembles have considerable freedom in choosing the relative weights of different chain lengths N. The details are as follows: Fixed-N, fixed-x ensemble. The state space is ff^(x), and the probability distribution is TT(U;) = \/CN(X] for each u e ^(x). Fixed-N, variable-x ensemble. The state space is «$*#, and the probability distribution is TT(W) = l/cj\r for each uj 6 S^Moo Variable-N, fixed-x ensemble. The state space is £f(x] = {^^(x), and the probability distribution is generally taken to be ff-o where
"It is sometimes called the "polymer-magnet analogy", but this phrase is misleading: at least for SAWs (athermal linear polymers), the correspondence is an exact mathematical identity (Ref. 11, Section 2.3), not merely an "analogy". tThe proper terminology for these ensembles is unclear to me. The fixed-length and variablelength ensembles are sometimes called "canonical" and "grand canonical", respectively (based on considering the monomers as particles). On the other hand, it might be better to call these ensembles "microcanonical" and "canonical", respectively (considering the polymers as particles and the chain length as an "energy"), reserving the term "grand canonical" for ensembles of many SAWs. My current preference is to avoid entirely these ambiguous terms, and simply say what one means: "fixed-length", "variable-length", etc.
56
M O N T E C A R L O M E T H O D S FOR THE SAW
Here p > 0 is a fixed number (usually 0 or 1), and /? is a monomer fugacity that can be varied between 0 and (3C = 1 /p,. By tuning /3 we can control the distribution of walk lengths N. Indeed, from eq. (2.3) we have
as /3 | /3C, provided that;? + asing > 1.* Therefore, to generate a distribution of predominantly long (but not too long) walks, it suffices to choose /3 slightly less than (but not too close to) (3C. Variable-N, variable-x ensemble. T h e state space i s a n d t h e probability distribution is generally taken to be where
p and (3 are as before, and from eq. (2.2) we have
as /? t PC- (Here the condition j? + 7 > 0 is automatically satisfied, as a result of the rigorous theorem 7 > I.11) An unusual two-SAW ensemble is employed in the join-and-cut algorithm, as will be discussed in Section 2.6.6.2. 2.3 Monte Carlo methods: a review
Monte Carlo methods can be classified as static, quasi-static or dynamic. Static methods are those that generate a sequence of statistically independent samples from the desired probability distribution TT. Quasi-static methods are those that generate a sequence of statistically independent batches of samples from the desired probability distribution TT; the correlations within a batch are often difficult to describe. Dynamic methods are those that generate a sequence of correlated samples from some stochastic process (usually a Markov process) having the desired probability distribution TT as its unique equilibrium distribution. In this section we review briefly the principles of both static and dynamic Monte Carlo methods, with emphasis on the issues that determine the statistical efficiency of an algorithm.
*If 0 < p + asing < 1, then (N) ~ (1 - j3^i\ fr+Q'«) as fi t ft, with logarithmic corrections when P + asing = 0, 1. If p + asing < 0, then (A^) remains bounded as /3 | A-.
MONTE CARLO METHODS: A REVIEW
57
2.3.1 Static Monte Carlo methods Consider a system with state space (configuration space) S; for notational simplicity, let us assume that S is discrete (i.e., finite or countably infinite). Now let TT = {^x}x(-s t>e a probability distribution on S, and let A = {A(x)}xeS be a real-valued observable. Our goal is to devise a Monte Carlo algorithm for estimating the expectation value
The most straightforward approach (standard Monte Carlo) is to generate independent random samples Xi,...,Xn from the distribution TT (if one can!), and use the sample mean
as an estimate of ^}
. This estimate is unbiased, i.e.,
Its variance is
However, it is also legitimate to generate samples X\,...,Xn from any probability distribution v, and then use weights W(x) = KX/VX- There are two reasons one might want to sample from v rather than -n. Firstly, it might be unfeasible to generate (efficiently) random samples from TT, so one may be obliged to sample instead from some simpler distribution v. This situation is the typical one in statistical mechanics. Secondly, one might aspire to improve the efficiency (i.e., reduce the variance) by sampling from a cleverly chosen distribution v. There are two cases to consider, depending on how well one knows the function W(x)\
(b) W(x) is known except for an unknown multiplicative constant (normalization factor). This case is common in statistical mechanics: if TTX = Z^e-WW and vx = Z^V'^M, then W(x) = (Zpi /Zf))e~~^~P'}H^ but we are unlikely to know the ratio of partition functions.
58
M O N T E C A R L O M E T H O D S FOR THE SAW
In the first case, we can use as our estimator the weighted sample mean
This estimate is unbiased, since Its variance is
This estimate can be either better or worse than standard Monte Carlo, depending on the choice of v. The optimal choice is the one that minimi/es (WA2^ subject to the constraint (W7""1) = 1, namely
or in other words vx = const x |^(^)|TT X . In particular, if A(x) > 0 the resulting estimate has zero variance. But it is impractical: in order to know W(x) we must know the denominator in eq. (2.28), which is the quantity we were trying to estimate in the first place! Nevertheless, this result offers some practical guidance: we should choose W(x)~l to mimic |^4(x)| as closely as possible, subject to the constraint that ^jnxW(x)~ be calculable analytically (and equal to 1). -xes In the second case, we have to use a ratio estimator
here the unknown normalization factor in W cancels out. This estimate is slightly biased: using the small-fluctuations approximation
we obtain
MONTE CARLO METHODS: A REVIEW
59
Since the bias is of order 1 /«, while the standard deviation (= square root of the variance) is of order l/^/n, the bias is normally negligible compared to the statistical fluctuation.* The variance can also be computed by the smallfluctuations approximation
it is
The optimal choice of v is the one that minimizes {W(A — (-4) ) ) subject to the constraint (W~1^ = I, namely
Let us now try to interpret these formulae. First note that with equamy omy n v = TT. so ^ w f "mismatch" (or "distance") between that A is a bounded observable, i.e., immediate from eqs (2.27) and (2.33)
— i measures, in a rougn sense, me v and IT. Now assume for simplicity \A(x)\ < M for all x € S. Then it is that
V
So the variances cannot get large unless (W} ^> 1, i.e., v is very distant from TT; and in this case it is easy to see that the variances can get large. The * Note that (with equality if and only if A — c\ + ci W~1) by the Schwarz inequality with measure v applied to the functions W' — 1 and W(A — (A)^). Therefore, from eqs (2.31) and (2.33) we have (to leading order in l/«) So the bias is , so the useful sample size is much smaller than the total sample size. Here is a concrete example: Let S be the set of all TV-step walks (not necessarily self-avoiding) starting at the origin. Let TT be uniform measure on self-avoiding walks, i.e.
Unfortunately, it is not easy to generate (efficiently) random samples from TT (that is the subject of this chapter!). So let us instead generate ordinary random walks, i.e., random samples from and then apply the weights W(u)} = 7rw/tv Clearly we have
which grows exponentially for large N. Therefore, the efficiency of this algorithm deteriorates exponentially as N grows. The reader is referred to Chapter 5 of Ref. 63 for some more sophisticated static Monte Carlo techniques. It would be interesting to know whether any of them can be applied usefully to the self-avoiding walk.
2.3.2 Dynamic Monte Carlo methods In this subsection we review briefly the principles of dynamic Monte Carlo methods, and define some quantities (autocorrelation times) that will play an important role in the remainder of this article. The idea of dynamic Monte Carlo methods is to invent a stochastic process with state space S having TT as its unique equilibrium distribution. We then simulate this stochastic process, starting from an arbitrary initial configuration; once the system has reached equilibrium, we measure time averages, which converge (as the run time tends to infinity) to 7r-averages. In physical terms, we are inventing a stochastic time evolution for the given system. It must be emphasized, however, that this time evolution need not correspond to any real "physical" dynamics', rather, the dynamics is simply a numerical algorithm, and it is to be chosen, like all numerical algorithms, on the basis of its computational efficiency.
MONTE CARLO METHODS: A REVIEW
61
In practice, the stochastic process is always taken to be a Markov process. We assume that the reader is familiar with the elementary theory of discretetime Markov chains.* For simplicity let us assume that the state space S is discrete (i.e. finite or countably infinite); this is the case in nearly all the applications considered in this chapter. Consider a Markov chain with state space S and transition probability matrix P = {p(x —> y)} = {pxy} satisfying the following two conditions: (A) For each pair x, y e S, there exists an n > 0 for which p$y > 0. Here p^xy = (P")xy is the n-step transition probability from x to y. [This condition is called irreducibility (or ergodicity); it asserts that each state can eventually be reached from each other state.] (B) For each y e S,
(This condition asserts that TT is a stationary distribution [or equilibrium distribution} for the Markov chain P — {pxy}.) In this case it can be shown66 that TT is the unique stationary distribution for the Markov chain P = {pxy}, and that the occupation-time distribution over long time intervals converges (with probability 1) to TT, irrespective of the initial state of the system. If, in addition, P is aperiodic [this means that for each pair x,y € S, p"y > 0 for all sufficiently large n], then the probability distribution at any single time in the far future also converges to TT, irrespective of the initial state—that is, lim^oo p^y = iry for all x. Thus, simulation of the Markov chain P provides a legitimate Monte Carlo method for estimating averages with respect to TT. However, since the successive states XQ,XI, ... of the Markov chain are in general highly correlated, the variance of estimates produced in this way may be much higher than in independent sampling. To make this precise, let A = {A(x)}xeS be a real-valued function defined on the state space S (i.e., a real-valued observable) that is square-integrable with respect to TT. Now consider the stationary Markov chain (i.e., start the system in the stationary distribution TT, or equivalently, "thermalize" it for a very long time prior to observing the system). Then {At} = {A(X,)} is a stationary stochastic process with mean
"The books of Kemeny and Snell64 and losifescu65 are excellent references on the theory of Markov chains with finite state space. At a somewhat higher mathematical level, the books of Chung66 and Nummelin67 deal with the cases of countable and general state space, respectively.
62
M O N T E C A R L O M E T H O D S FOR THE SAW
and unnormalized autocorrelation function*
The normalized autocorrelation function is then Typically PAA(I) decays exponentially (~ e~\'\lr) for large t; we define the exponential autocorrelation time
and
Thus, rexp is the relaxation time of the slowest mode in the system. (If the state space is infinite, rexp might be +oo!)t On the other hand, for a given observable A we define the integrated autocorrelation time
*In the statistics literature, this is called the autocovariance function. ^An equivalent definition, which is useful for rigorous analysis, involves considering the spectrum of the transition probability matrix P considered as an operator on the Hilbert space / 2 (7r). [/2(?r) is the space of complex-valued functions on S that are square-integrable with respect to IT. \\A\\ = (J2x€Snx\A(x)\2)1/2 < oo. The inner product is given by (A,B) = Xltes K,A(x)*B(x)] It is not hard to prove the following facts about P: (a) The operator P is a contraction. (In particular, its spectrum lies in the closed unit disk.) (b) 1 is a simple eigenvalue of P, as well as of its adjoint P*, with eigenvector equal to the constant function 1. (c) If the Markov chain is aperiodic, then 1 is the only eigenvalue of P (and of P*) on the unit circle. (d) Let R be the spectral radius of P acting on the orthogonal complement of the constant functions: Then R = e~l/T"'. Facts (a)-(c) are a generalized Perron-Frobenius theorem68; fact (d) is a consequence of a generalized spectral radius formula.69 Note that the worst-case rate of convergence to equilibrium from an initial uilibrium distribution is controlled by R, and hence noneq by r
MONTE CARLO METHODS: A REVIEW
63
(The factor of j is purely a matter of convention; it is inserted so that Tint,A ~ TexpiA if pAA(t) ~ e~'''/ r with T » 1.) The integrated autocorrelation time controls the statistical error in Monte Carlo estimates of (A). More precisely, the sample mean
has variance
Thus, the variance of A is a factor 2TM,A larger than it would be if the {At} were statistically independent. Stated differently, the number of "effectively independent samples" in a run of length n is roughly n/2rinttAIn summary, the autocorrelation times rexp and Tint^ play different roles in Monte Carlo simulations. rexp controls the relaxation of the slowest mode in the system; in particular, it places an upper bound on the number of iterations Hdisc which should be discarded at the beginning of the run, before the system has attained equilibrium (e.g., n^c ~ 20rexp is usually more than adequate). On the other hand, TinttA determines the statistical errors in Monte Carlo estimates of (A), once equilibrium has been attained. Most commonly it is assumed that rexp and TinttA are of the same order of magnitude, at least for "reasonable" observables A. But this is not true in general. In fact, one usually expects the autocorrelation function PAA(*) to obey a dynamic scaling law70 of the form
valid in the limit
Here a, b > 0 are dynamic critical exponents and F is a suitable scaling function; 0 is some "temperature-like" parameter, and 0C is the critical point. Now suppose that F is continuous and strictly positive, with F(x) decaying rapidly (e.g., exponentially) as \x\ —> oo. Then it is not hard to see that
64
MONTE C A R L O M E T H O D S FOR THE SAW
so that TexptA and T^A have different critical exponents unless a = 0.* Actually, this should not be surprising: replacing "time" by "space", we see that rexpiA is the analogue of a correlation length, while Tint^ is the analogue of a susceptibility; and eqs (2.54)-(2.56) are the analogue of the well-known scaling law 7 = (2 - rj)v—clearly 7 ^ v in general! So it is crucial to distinguish between the two types of autocorrelation time. Returning to the general theory, we note that one convenient way of satisfying the stationarity condition (B) is to satisfy the following stronger condition:
(Summing (B') over x, we recover (B).) (B') is called the detailed-balance condition; a Markov chain satisfying (B') is called reversible.^ (B') is equivalent to the self-adjointness of P as on operator on the space / 2 (7r). In this case, it follows from the spectral theorem that the autocorrelation function CAA(I) has a spectral representation
with a nonnegative spectral weight do^(A) supported on the interval [_ e -iA>xM 5 e-i/T«M] jt follows that
There is no particular advantage to algorithms satisfying detailed balance (rather than merely satisfying stationarity), but they are easier to analyze mathematically. Finally, let us make a remark about transition probabilities P that are "built up out of other transition probabilities P\,Pi, • • •,Pn'(a) If P\, ?2, • • • , Pn satisfy the stationarity condition (resp. the detailedbalance condition) for TT, then so does any convex combination P = ELi W Here A,- > 0 and £Z=i A, = 1.
*Our discussion of this topic in Ref. 71 is incorrect. A correct discussion can be found in Ref. 72. ^For the physical significance of this term, see Kemeny and Snell (Ref. 64, section 5.3) or losifescu (Ref. 65, section 4.5).
STATIC M O N T E CARLO M E T H O D S FOR THE SAW
65
(b) If JPi, Pa, • • • j Pn satisfy the stationarity condition for TT, then so does the product P = PI Pa • • • Pn. (Note, however, that P does not in general satisfy the detailed-balance condition, even if the individual P, do.*) Algorithmically, the convex combination amounts to choosing randomly, with probabilities {A,-}, from among the "elementary operations" P,-. (It is crucial here that the A, are constants, independent of the current configuration of the system; only in this case does P leave TT stationary in general.) Similarly, the product corresponds to performing sequentially the operations Pi,P2,...,P«-
2.4 Static Monte Carlo methods for the SAW 2.4.1 Simple sampling and its variants The most obvious static technique for generating a random A^-step SAW is simple sampling: just generate a random Af-step ordinary random walk (ORW), and reject it if it is not self-avoiding; keep trying until success. It is easy to see that this algorithm produces each Af-step SAW with equal probability. Of course, to save time we should check the self-avoidance as we go along, and reject the walk as soon as a self-intersection is detected. (Methods for testing self-avoidance are discussed in Section 2.7.1.2.) The algorithm is thus: title Simple sampling. function ssamp (N) comment This routine returns a random JV-step SAW.
start:
w0
