# What is condensed matter physics pdf torrent

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Chaichian, A. QFT, statistical physics and modern applications - Chaichian M. Nash, S. Arias, M. Gates, et al. Greiner, J. DiFrancesco, P. Mathieu, D. Damagaard, J. Borne, G. Lochak, H. Greiner, S. Schramm, E. Clarkson, D. Baez, J. Gambini, J. Vinogradov, P.

Smith, E. Greiner, B. Feynman, A. Wave Equations, 3rd ed. Saltsidis, B. Walls, G. Gauglitz , T. Aharony, S. Gubser, J. Maldacena, H. Ooguri, Y. Antonov, B. Harmon, A. Hartmann, H. Born, E. Hansen , Chris R. Hirth, J. Pitts, L. Gould, J. Greiner, L. Niese, H. Torrent downloaded from Demonoid. Drummond, and P. Hammond, W.

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James, S. Kenny, and W. Foulkes, Phys. B 51, Needs, S. Kenny, W. Foulkes, and A. James, Phys. Tosi, in: Solid State Physics, Vol. Ehrenreich and D. Turnbull Academic, New York,. Fr as er, W. Foulkes, G. Kenny, and A. Williamson, Phys. Williamson, G. Needs, L. Foulkes, Y. Wang, and M. Chou, Phys. Drummond, First Year Ph. Report, University of Cambridge. Metropolis, A. Rosenbluth, M. Rosenbluth, A.

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Cossi, G. Scalmani, N. Rega, G. Petersson, H. Nakatsuji, M. Hada, M. Ehara, K. Toyota, R. H as egawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, M. Klene, X. Li, J. Knox, H. Cross, C. Adamo, J. Jaramillo, R. Gomperts, R. Stratmann, O. Yazyev, A. Austin, R. Pomelli, J. Ochterski, P. Ayala, K. Morokuma, G. Voth, P. Salvador, J. Zakrzewski, S. Dapprich, A. Daniels, M. Strain, O. Fark as , D. Malick, A. Raghavachari, J. Foresman, J. Ortiz, Q. Cui, A. Baboul, S. Clifford, J. Cioslowski, B.

Liu, A. Li as henko, P. Piskorz, I. Komaromi, R. Martin, D. Fox, T. Keith, M. Al-Laham, C. Nanayakkara, M. Challacombe, P. Gill, B. Johnson, W. Chen, M. Wong, C. Gonzalez, and. Saunders, R. Dovesi, C. Roetti, R. Orlando, C. Zicovich-Wilson, N. Harrison, K. Civalleri, I. Bush, Ph. Leung, R. Needs, G. Rajagopal, S. Itoh, and S. Ihara, Phys. Segall, P. Lindan, M. Probert, C.

Pickard, P. H as nip, S. Clark, and M. Payne, J. Gonze, J. Carac as , F. Detraux, M. Fuchs, G. Rignanese, L. Sindic, M. Verstraete, G. Zerah, F. Jollet, M. Roy, M. Mikami, Ph. Ghosez, J. Raty, and D. Allan, Comput.

Skylaris, A. Most of i, P. Haynes, O. Payne, Phys. B 66, Haynes, C. Skylaris, and M. Ahlrichs, M. Horn, and C. Pople, M. Head-Gordon, D. Fox, K. Raghavachari, and L. Curtiss, J. Curtiss, C. Jones, G. Trucks, K. Raghavachari, and J. Pople, J. Curtiss, K. Raghavachari, P. Redfern, and J. Whitlock, D. Ceperley, G. Chester, and M. Kalos, Phys. B 19, Ari as de Saavedra and M. E 67, Ss, Ar, Nc Quantum Monte Carlo is an important and complementary alternative to density functional theory when performing computational electronic structure calculations in which high accuracy is required.

You have already flagged this document. Thank you, for helping us keep this platform clean. The editors will have a look at it as soon as possible. Self publishing. Share Embed Flag. TAGS calculations functions orbitals monte carlo method quantum electrons scaling verlag download condensed www.

You also want an ePaper? The method h as many attractive features for probing the electronic structure of real atoms, molecules and solids. In particular, it is a genuine many-body theory with a natural and explicit description of electron correlation which gives consistent, highly-accurate results while at the same time exhibiting favourable cubic or better scaling of computational cost with system size. KGaA, Weinheim M.

The various different techniques which lie within its scope have in common the use of random sampling, and this is used because it represents by far the most efficient way to do numerical integrations of expressions involving wave functions in many dimensions. As we shall see, VMC is simple in concept and is designed just to sample a given trial wave function and calculate the expectation value of the Hamiltonian using Monte Carlo numerical integration.

This is more useful than it sounds since the method is variational and thus we can to some extent optimize suitably parametrized explicitly correlated wave functions using standard techniques. Other variants, including those aimed at expanding the scope of the method to finite temperature such as path integral Monte Carlo PIMC [5, 6], or those designed to find the exact non-relativistic energy overcoming the small fixed-node approximation made in DMC such as fermion Monte Carlo FMC [7—9] will not be discussed in any detail here.

The interested reader is invited to consult the literature for more detailed discussions the extensive bibliography in Ref. In its early days QMC w as perhaps best known for its application to the homogeneous electron g as by Ceperley and Alder [10]. The results of these calculations were generally understood to be extremely accurate and were used to develop accurate parametrizations of the local density approximation to density functional theory DFT in the early s.

However, it is of course perfectly possible to apply the method to real systems with atoms, and for small molecules containing helium and hydrogen QMC gives total energies with a remarkable accuracy greater than 0. Despite such capabilities the technology of QMC is neither mature nor particularly widely used; its routine application to arbitrary finite and periodic systems, particularly those containing heavier atoms, h as long been just out of reach and there are still many open methodological and algorithmic problems to interest the computational electronic structure theorist.

The situation is clearly changing however, and it ought now to be a matter of routine for people to perform accurate QMC calculations of even quite large systems, albeit starting from wave functions generated from one-electron molecular orbital or band theory. Systems and problems for which an accurate determination of the total energy actually matters, and for which DFT for example is not sufficiently accurate, are likely more numerous than is generally believed.

This code is capable of performing both variational and diffusion Monte Carlo calculations on a wide variety of systems, which may be of finite extent atoms or molecules or may obey periodic boundary conditions in one, two or three dimensions, modelling what one might respectively call polymers, slabs or surfaces and crystalline solids.

One of the more attractive features of QMC is the scaling behaviour of the necessary computational effort with system size. This is favourable enough that we can continue to apply the method to systems as large as are treated in conventional DFT, albeit with a considerably bigger pre-factor and thus probably not on the same computers.

In fact QMC seems currently to be the most accurate method available for medium-sized and large systems. KGaA, Weinheim www. The largest calculations done to date on the more expensive periodic systems using the regular algorithm include almost electrons per cell in the three-dimensional electron g as [12], electrons atoms per cell in crystalline silicon [13], and electrons atoms per cell in antiferromagnetic nickel oxide [14].

Furthermore the natural observation h as been made that provided localized molecular or crystalline orbitals are used in constructing the QMC trial wave function, and provided these orbitals are expanded in a localized b as is set, then the scaling of the b as ic algorithm can be substantially improved over implementations using delocalized functions such as Bloch orbitals and plane-wave b as is sets.

An improved scaling capability b as ed on such ide as , to be discussed in more detail in Section 4. Errors due to the use of a finite b as is set are expected to be small since the many-electron wave function is not represented directly in terms of a b as is set, but rather by the average distribution of an ensemble of particles evolving in imaginary time.

The sole purpose of the b as is set that is in fact employed in DMC is to represent a guiding function required for importance sampling. The final DMC energy depends only weakly on the nodal surface of this guiding function i. There are no memory or disk bottlenecks even for relatively large systems.

The method is size-consistent and variational. One may as k why one should formulate a method b as ed on the many-electron wave function when so much stress is normally placed on reducing the number of variables in the quantum problem by using, e.

In DFT, the complicated many-body problem is effectively relocated into the definition of the exchange-correlation functional, whose mathematical expression is not currently known and unlikely ever to be known exactly.

The inevitable approximations to this quantity substantially reduce the attainable accuracy. Standard widely-used solid-state texts of ten deny the possibility of doing this directly in any meaningful way for large crystalline systems. However the key simplifying physical idea to allow one to use, for example, QMC in crystalline solids is not the use of one-electron orbitals but simply the imposition of periodic boundary conditions. One can then www. Towler: The quantum Monte Carlo method have an explicitly correlated many-body wave function i.

It is clear that in order for this to have any chance of being an accurate approximation the range of the electron—electron pair correlation function must be substantially shorter than the repeat distance and the box must be large enough so that the forces on the particles within it are very close to those in the bulk. This problem is analagous to but not quite the same as the problem of representing an infinite system in DFT calculations. An additional type of finite-size error arises in periodic QMC calculations though not in DFT when calculating interactions between particles with long-range Coulomb sums.

The difference is that in QMC we deal with instantaneous positions of electron configurations, rather than with the interaction of averaged densities. When using the standard Ewald formulation [21, 22] for these long-range summations, the choice of boundary conditions equivalent to embedding your supposed hunk of crystal in a perfect conductor leads to an effective depolarization field which cancels the field due to your notional surface charges.

These can be substantially reduced by using special techniques [23]. A few years ago in his Nobel prize-winning address Walter Kohn suggested that the many-electron wave function is not a legitimate scientific concept when more than about a thousand particles are involved [24]. It would be pretty dis as trous if this meant that QMC could not be used for large systems, so let us try to understand what he means. The main idea behind his statement is that the overlap of any approximate wave function with the exact one will tend exponentially to zero as the number of particles incre as es unless one uses a wave function in which the number of parameters incre as es exponentially with system size, and that clearly such a wave function would not be computable for large systems.

This is indeed true, and one may e as ily verify it by calculating the overlap integral directly using VMC [25]. One can thus evaluate the overlap between, say, a single-determinant wave function and the same single-determinant function multiplied by a J as trow correlation function. Even though these objects share the same nodal surface, we still expect to see and indeed do see the result that Kohn predicts.

Luckily his objection seems not to be relevant to the sort of QMC calculations discussed here. Certainly the successful DMC calculations of systems containing up to electrons mentioned earlier suggest as much, but as Kohn himself points out, we are interested in quantities such as the total energy, which can be accurate even when the overlap with the exact wave function goes to zero.

To get the energy right it is required only that relatively low-order correlation functions such as the pair-correlation function are well-described and QMC seems to manage this very well. For a large system the overlap of the determinant of Kohn—Sham orbitals with the exact one will go to zero because of the inevitable numerical inaccuracies and the approximations to the exchange-energy functional.

Fortunately, as I have suggested, the overlap cat as trophe seems to be irrelevant to actually calculating most quantities of interest. To understand how accurate the total energies must be we note that the main goal is to calculate the energy difference between two arrangements of a set of atoms.

The desired result might be the energy required to form a defect, or the energy barrier to some process, or whatever. All electronic structure methods for large systems rely on a cancellation of errors in energy differences. For such error cancellations to occur we require that the error in the energy per atom is proportional to the number of atoms. If this condition w as not satisfied then, for example, the cohesive energy would not have a well-defined limit for large systems.

Additional requirements on QMC algorithms are that the number of parameters in the trial wave function must not incre as e too rapidly with system size and that the wave function be e as ily computable. Fortunately the number of parameters in a typical QMC trial wave function incre as es only linearly, or at worst quadratically, with system size and the function can be evaluated in a time which rises as a low power of the system size.

Towler: The quantum Monte Carlo method The question of whether or not we get the right answer with this approach is just one of complexity; can we create a wave function with enough variational freedom so that the energy approaches the exact non-relativistic ground state energy? This, to my mind, is the main use of VMC and in our laboratory we rarely use it as a method in its own right when performing calculations. Although the efficiency of the DMC calculations is greatly incre as ed with more accurate trial functions, the final DMC energy does not in principle depend on that part of the wave function that we generally optimize.

Through its direct influence on the variance of the energy the accuracy of the trial wave function thus determines the amount of computation required to achieve a specified accuracy. When optimizing wave functions, one can therefore choose to use energy or variance as the objective function to be minimized. We do not need to be able to integrate the wave function analytically as is done for example in quantum chemistry methods with Gaussian b as is functions.

We just need to be able to evaluate it at a point in the configuration space i. This being the c as e, we can use correlated wave functions which depend explicitly on the distances between particles. This consists of a single Slater determinant or sometimes a linear combination of a small number of them multiplied by a positive-definite J as trow correlation function which is symmetric in the electron coordinates and depends on the inter-particle distances.

The J as trow factor allows efficient inclusion of both long and short range correlation effects. As we shall see however, the final DMC answer depends only on the nodal surface of the wave function and this cannot be affected by the nodeless J as trow.

In DMC it serves mainly to decre as e the amount of computer time required to achieve a given statistical error bar and to improve the stability of the algorithm. Note that the use of wave function forms in QMC which allow one to treat non-collinear spin arrangements and the resultant vector magnetization density is an interesting open problem, and we are currently working on developing such an algorithm [28].

The exact functional form is quite complicated and there is no need to go into all the details here for the curious, they may be found in Ref. In the full inhomogeneous J as trow we generality optimize the coefficients of the various polynomial expansions which appear linearly in the J as trow factor and the cut of f radii of the various terms which are non-linear.

The linearity or otherwise of the various terms clearly h as a bearing on their e as e of optimization, a subject to which we now turn. In addition to the various J as trow parameters mentioned in the previous section, the CASINO code allows optimization of the coefficients of the determinants of a multi-determinant wave function, various parameters in specialized wave functions used e. So clearly the parameters appear in many different contexts, they need to be minimized in the presence of noise, and there can be many of them.

This makes the optimization a complicated t as k in general. Directly optimizing the orbitals in the presence of the J as trow factor is generally thought to be a good thing, since this in some sense optimizes the nodal surface and in so doing allows improvement of the DMC energy. The best way to do this in systems containing more than one atom remains an open problem however, though some progress h as been made [30, 31]. There is no re as on why one may not optimize the energy directly, and indeed it is generally believed that wave functions corresponding to the minimum energy have more desirable properties.

There are however a number of re as ons why variance minimization h as historically been generally preferred to energy minimization beyond the trivial fact that the variance h as a known lower bound of zero.

The most important of these is simply that it h as proved e as ier to design robust, numerically-stable algorithms to minimize the variance than it h as for the energy [32, 33]. We then use this information to calculate the objective function — in this c as e the variance — and proceed to minimize it by varying the parameters.

Note that the point of using the weights here is that we do not have to regenerate the set of configurations every time the parameter values are changed. However, having generated a new set of parameters with this algorithm, we can then carry out a second configuration generation run with these new, more accurate parameters followed by a second optimization, and so on. In the limit of perfect sampling, the reweighted variance is equal to the actual variance, and is therefore independent of the configuration distribution, so that the optimized parameters would not change over successive cycles.

There is a major problem with it however, and this arises from the fact that the weights may vary rapidly as the parameters change especially for large systems. This can lead to severe instabilities in the numerical procedure. Somewhat surprisingly perhaps, it usually turns out that the best solution to this is to do without the weights at all, in which c as e we are minimizing the unreweighted variance.

This turns out to have a number of advantages beyond improving the numerical stability. The self-consistent minimum in the unreweighted variance almost always turns out to give lower energies than the minimum in the reweighted variance. Furthermore our group h as recently demonstrated a new scheme which hugely speeds up the optimization of parameters that occur linearly in the J as trow, which are the most important in the wave functions that we use.

The b as is of this is that the unreweighted variance can be written analytically as a quartic function of the linear parameters. The whole procedure of variance minimization can be, and in CASINO is, thoroughly automated and providing a systematic approach is adopted, optimizing VMC wave functions is not the complicated time-consuming business it once w as.

This is particularly the c as e if one requires the optimized wave function only for input into a DMC calculation, in which c as e one need not be overly concerned with lowering the VMC energy as much as possible. In practice therefore, the main use of VMC is in providing the optimized trial wave function required as an importance sampling function by the much more powerful DMC technique, which we now describe. If we tried to calculate the expectation value of the Hamiltonian using VMC we would obtain an energy which w as substantially in error.

What DMC can do, in essence, is to correct the functional form of the guessed square box wave function so that it looks like the correct exponentially-decaying one before calculating the expectation value. This is a nice trick if you can do it, particularly when we have very little practical idea of what the exact ground state wave function looks like that is, almost always. As one might expect, the algorithm is necessarily rather more involved than that for VMC. The required rate of removing or adding configurations diverges when the potential energy diverges, which occurs whenever two electrons or an electron and a nucleus are coincident.

This leads to extremely poor statistical behaviour. Towler: The quantum Monte Carlo method These problems are dealt with at a single stroke by introducing an importance sampling transformation. The nodal surface of a wave function is the surface on which it is zero and across which it changes sign. The problem of the poor statistical behaviour due to the divergences in the potential energy is also solved because the term V R - E S in Eq.

This theorem is intimately connected with the existence of a variational principle for the DMC ground state energy [38]. The accuracy of the fixed node approximation can be tested on small systems and normally leads to very satisfactory results. The trial wave function limits the final accuracy that can be obtained because of the fixednode approximation and it also controls the statistical efficiency of the algorithm.

In the lower panel, the noisy black line is the local energy after each move, the green line is the current best estimate of the DMC energy, and the red line is E T in Eq. As the simulation equilibrates the best estimate of the energy, initially equal to the VMC energy, decre as es significantly then approaches a constant — the final DMC energy. The upper panel shows the variation in the population of the ensemble during the simulation as walkers are created or destroyed.

In the ground state of the carbon pseudo-atom, for example, a single Hartree—Fock determinant retrieves about By definition a determinant of Hartree—Fock orbitals gives the lowest energy of all singledeterminant wave functions and DFT orbitals are of ten very similar to them. These orbitals are not optimal when a J as trow factor is included, but it turns out that the J as trow factor does not change the detailed structure of the optimal orbitals very much, and the changes are well described by a fairly smooth change to the orbitals.

CASINO is capable of directly optimizing the atomic orbitals in a single atom by optimizing a parametrized function that is added to the self-consistent orbitals [39]. This w as found to be useful only in certain c as es. In atoms one of ten sees an improvement in the VMC energy but not in DMC, indicating that the Hartree—Fock nodal surface is close to optimal even in the presence of a correlation function. Unfortunately direct optimization of both the orbitals and J as trow factor cannot e as ily be done for large polyatomic systems because of the computational cost of optimizing large numbers of parameters, and so it is difficult to know how far this observation extends to more complex systems.

One promising tech- www. Towler: The quantum Monte Carlo method nique [30, 31] is to optimize the potential that generates the orbitals rather than the orbitals themselves. Another possible way to improve the orbitals over the Hartree—Fock form, suggested by Grossman and Mit as [40], is to use a determinant of the natural orbitals which diagonalize the one-electron density matrix.

It is not immediately clear why this should be expected to work in QMC however — the motivation appears to be that the convergence of configuration interaction expansions is improved by using natural orbitals instead of Hartree—Fock orbitals.

The calculation of re as onably accurate natural orbitals is unfortunately computationally demanding, and this makes such an approach less attractive for large systems. It should be noted that all such techniques which move the nodal surface of the trial function and hence potentially improve the DMC energy make wave function optimization with fixed configurations more difficult.

The nodal surface deforms continuously as the parameters are changed, and in the course of this deformation the fixed set of electron positions of one of the configurations may end up being on the nodal surface. In some c as es it is necessary to use multi-determinant wave functions to preserve important symmetries of the true wave function.

In other c as es a single determinant may give the correct symmetry but a significantly better wave function can be obtained by using a linear combination of a few determinants. Multi-determinant wave functions have been used successfully in QMC studies of small molecular systems and even in periodic calculations such as the recent study of the neutral vacancy in diamond due to Hood et al.

However other studies have shown that while using multideterminant functions gives an improvement in VMC, this sometimes does not extend to DMC, indicating that the nodal surface h as not been improved [39]. It is widely believed that a direct expansion in determinants as used in, for example, configuration interaction calculations converges very slowly because of the difficulty in describing the strong correlations which occur when electrons are close to one another.

These correlations result in cusps in the wave function when two electrons are coincident, which are not well approximated by a finite sum of smooth functions [42]. In any c as e the number of determinants required to describe the wave function to some fixed accuracy incre as es exponentially with the system size; for some molecular c as es billions of determinants have been used. Ordinarily one might think that an expansion which required so many terms is not a very good expansion, because the b as is functions look nothing like the function that is being expanded, but this viewpoint h as historically not been popular in the quantum chemistry community.

As far as QMC is concerned, this would seem to rule out the possibility of retrieving a significant extra fraction of the correlation energy with QMC in large systems via an expansion in determinants. Methods in which only local correlations are taken into account might be helpful, but overall an expansion in determinants is not a promising direction to pursue for making QMC trial wave functions for large systems.

Backflow correlations were originally derived from a current conservation argument by Feynman [44], and Feynman and Cohen [45] to provide a picture of the excitations in liquid 4 He and the effective m as s of a 3 He impurity in 4 He. In a modern context they can also be derived from an imaginary-time evolution argument [46, 47]. In the backflow trial function the electron coordinates r i appearing in the Slater determinants of Eq.

Kwon, Ceperley, and Martin [46, 48] found that the introduction of backflow significantly lowered the VMC and DMC energies of the two and three-dimensional uniform electron g as at high densities. The use of backflow h as also been investigated for metallic hydrogen [49].

One interesting thing that we found is that energies obtained from VMC with backflow approached those of DMC without backflow. VMC with backflow may thus represent a useful level of theory since it is significantly less expensive than DMC. This is largely because every element of the Slater determinant h as to be recomputed each time an electron is moved, where as only a single column of the Slater determinant h as to be updated after each move when the b as ic Slater—J as trow wave function is used.

The b as ic scaling of the algorithm with backflow is thus N 4 rather than N 3. Backflow functions also introduce more parameters into the trial wave function, making the optimization procedure more difficult and costly. However the reduction in the variance normally observed with backflow greatly improves the statistical efficiency of QMC calculations, i. In our Ne atom calculations [39], for example, it w as observed that the computational cost per move in VMC and DMC incre as ed by a factor of between four and seven, but overall the time taken to complete the calculations incre as ed only by a factor of two to three.

Finally, it should be noted that backflow is expected to improve the QMC estimates of all expectation values, not just the energy, so on the whole it appears to be a good thing. The importance of using good quality single-particle orbitals in building up the Slater determinants in the trial wave function is clear.

The determinant part accounts for by far the most significant fraction of the variational energy. However, the evaluation of the single-particle orbitals and their first and second derivatives can sometimes take up more than half of the total computer time, and consideration must therefore be given to obtaining accurate orbitals which can be evaluated rapidly at arbitrary points in space. It is not difficult to see that the most critical thing is to expand the single-particle orbitals in a b as is set of localized functions.

This ensures that beyond a certain system size, only a fixed number of the localized functions will give a significant contribution to a particular orbital at a particular point. The cost of evaluating the orbitals does not then incre as e rapidly with the size of the system. An alternative procedure is to tabulate the orbitals and their derivatives on a grid, and this is fe as ible for small systems such as atoms, but for periodic solids or larger molecules the storage requirements quickly become enormous.

This is an important consideration when using parallel computers as it is much more efficient to store the single-particle orbitals on every node. Historically a very large proportion of condensed matter electronic structure theorists have used plane-wave b as is sets in their DFT calculations.

However in QMC, plane-wave expansions are normally extremely inefficient because they are not localized in real space; every b as is function contributes at every point, and the required number of functions incre as es linearly with system size. Only if there is a short repeat length in the problem are plane waves not totally unre as onable.

Note that this does not mean that all plane-wave DFT codes are useless for generating trial wave functions for CASINO; a post-processing utility can be used to reexpand a function expanded in plane-waves in another localized b as is before the wave function is input into CASINO.

These are localized, quick to evaluate, and are available from a wide-range of sophisticated s of tware www. Towler: The quantum Monte Carlo method packages. Such a large expertise h as been built up within the quantum chemistry community with Gaussians that there is a significant resistance to using any other type of b as is.

A great many Gaussian-b as ed packages have been developed by quantum chemists for treating molecules. In addition to the regular single determinant methods, these codes include various techniques involving multi-determinant correlated wave functions although sadly, not QMC!

This makes them very flexible tools for developing accurate molecular trial wave functions. For Gaussian b as is sets with periodic boundary conditions, the CRYSTAL program [54] can perform all-electron or pseudopotential Hartree—Fock and DFT calculations both for molecules and for systems with periodic boundary conditions in one, two or three dimensions, which makes it very useful as a tool for generating trial functions for CASINO.

Although QMC scales very favourably with system size it h as been estimated that the scaling of all-electron calculations with the atomic number Z is approximately Z. The use of a pseudopotential serves to reduce the effective value of Z and although errors are inevitably introduced, the gain in computational efficiency is sufficient to make applications to heavy atoms fe as ible.

Accurate pseudopotentials for single-particle theories such as DFT or Hartree—Fock theory are well developed, but pseudopotentials for correlated wave function techniques such as QMC present additional challenges. The first is that the shorter length scale variations in the wave function near a nucleus of large Z require the use of a small time step. The second problem is that the fluctuations in the local energy tend to be large near the nucleus because both the kinetic and potential energies are large.

The central idea of pseudopotential theory is to create an effective potential which reproduces the effects of both the nucleus and the core electrons on the valence electrons. This is done separately for each of the different angular momentum states, so the pseudopotential contains angular momentum projectors and is therefore a non-local operator.

It is convenient to divide the pseudopotential for each atom into a local part V ps loc r common to all angular momenta and a correction, V ps nl , l r , for each angular momentum l. It is therefore currently necessary to use pseudopotentials generated within some other framework. Possible schemes include Hartree—Fock theory and local DFT, where there is a great deal of experience in generating accurate pseudopotentials.

The problem with DFT pseudopotentials appears to be that they already include a local description of correlation which is quite different from the QMC description. Hartree—Fock theory, on the other hand, does not contain any effects of correlation. The QMC calculation puts back the valence-valence correlations but neglects core—core correlations which have only an indirect and small effect on the valence electrons and core-valence correlations.

Core-valence correlations are significant when the core is highly polarizable, such as in alkali-metal atoms. Another issue is that relativistic effects are important for heavy elements. CPPs have been generated for a wide range of elements see, e. Many Hartree—Fock pseudopotentials are available in the literature, mostly in the form of sets of parameters for fits to Gaussian b as is sets. Unfortunately many of them diverge at the origin, which can lead to significant time step errors in DMC calculations [61].

We concluded that none of the available sets are ideal for QMC calculations and that it would be helpful if we generated an on-line periodic table of smooth non-divergent Hartree—Fock pseudopotentials with relativistic corrections. This project h as now been completed by Trail and Needs, and is described in detail in Refs. If this cusp is represented accurately in the QMC trial wave function therefore, then the fluctuations in the local energy referred to in the previous section will be greatly reduced.

Now if numerical orbitals are used it is relatively e as y to produce an accurate representation of the cusp. However, as we have already remarked, such representations cannot really be used for large polyatomic systems because of the excessive storage requirements. In practice one finds that the local energy h as wild oscillations close to the nucleus which can lead to numerical instabilities in DMC calculations.

To solve this problem we can make small corrections to the single particle orbitals close to the nuclei which impose the correct cusp behaviour. Such corrections need to be applied at each nucleus for every orbital which is larger than a given tolerance at that nucleus.

Our scheme is b as ed on the idea of making the one-electron part of the local energy www. The scheme need only be applied to the s-component of orbitals centred at the nuclear position in question. To see the cusp corrections in action, let us first look at a hydrogen atom where the b as is set h as been made to model the cusp very closely by using very sharp Gaussians with high exponents.

Visually top left in Fig. If we zoom in on the region close to the nucleus top right we see the problem: the black line is the orbital expanded in Gaussians, the red line is the cusp-corrected orbital. The effect on the gradient and local energy is clearly significant. This scheme h as been implemented within the CASINO code both for finite and for periodic systems, and produces a significant reduction in the computer time required to achieve a specified error bar, as one can appreciate from Fig.

In order to understand our capability to do all-electron DMC calculations for heavier atoms, and to understand how the necessary computer time scales with atomic number, we performed calculations for various noble g as atoms [55]. By ensuring that the electron—nucleus cusps were accurately represented it 0.

The cusp corrections are imposed only in the figure on the right. QMC methods are stoch as tic and therefore yield mean values with an as sociated statistical error bar. We might want to calculate the energy of some system and compare it with the energy of a different arrangement of the atoms. The desired result might be a defect formation energy, an energy barrier, or an excitation energy. These are evidently energy differences which become independent of the system size when the system is large enough.

There are other quantities such as cohesive energies, lattice constants, and el as tic constants, for example, in which both energy and error bar may be defined per atom or per formula unit, in which c as e the error bar on the whole system is allowed to scale linearly with system size, i. As is well known, the best possible scaling for conventional non-stoch as tic single-particle methods such as DFT is O N [66]. A considerable effort h as been made over the previous decade to design DFT codes which a scale linearly with system size, b are f as ter than the regular cubic scaling algorithm for re as onable system sizes, and c are as accurate as codes using the regular algorithm, with the latter two problems being the most difficult.

In wave function-b as ed QMC, these additional problems do not occur; with the improved scaling algorithms described here the speed benefit is immediate and there is essentially no 2 loss of accuracy. However, for the scaling one cannot do better than O N in general, unless the desired quantity is expressible as an energy per atom. The statistical noise in the energy adds incoherently over the particles, so the variance in the mean energy incre as es as N and thus the error bar as N.

How- www. Towler: The quantum Monte Carlo method 2 3 ever, O N scaling is still a v as t improvement over O N scaling when N can be of the order of a few thousand, and clearly the scaling is improved further for properties which can be expressed in terms of 3 2 energies per atom. The operations which make up this term are 1 evaluation of the orbitals in the Slater determinants, 2 evaluation of the J as trow factor, and 3 evaluation of Coulomb interactions between particles.

The first of these operations is by far the most costly. As in O N -DFT methods, the solution is to use localized orbitals instead of the delocalized single-particle orbitals that arise naturally from standard DFT calculations. The number of such orbitals contributing at a point in space is independent of N which leads to the required improvement in scaling. An impartial evaluation of the two different methods [69] showed that the latter w as superior, and this w as the approach finally adopted for the production version of CASINO.

For the J as trow factor all that is required to achieve the improved scaling is that it be truncated at some distance which is independent of system size. Because the correlations are essentially local it is natural to truncate the J as trow factor at the radius of the exchange-correlation hole. Of course, truncating the J as trow factor does not affect the final answer obtained within DMC because it leaves the nodal surface of the wave function unchanged, although if it is truncated at too short a distance the statistical noise incre as es.

The scaling of the Coulomb interactions can be improved using an accurate scheme which exploits the fact that correlation is short-ranged to replace the long-range part by its Hartree contribution in the style of the Modified Periodic Coulomb MPC interaction [23].

This arises from N updates of the matrix of c of actors of the inverse Slater matrix required when computing the ratio of 2 new to old determinants after each electron move , each of which takes a time proportional to N , plus the extra factor of N from the statistical noise.

In CASINO this operation h as been significantly streamlined through the use of sparse matrix techniques and we have not yet found a system where it contributes substantially to the overall CPU time. Taken together the localization algorithms described above should speed up continuum fermion QMC calculations significantly for large systems, but we can view it in another light — as an embedding algorithm in which a QMC calculation could be embedded within a DFT one.

The idea is to use the higher accuracy of QMC where it is most needed, such as around a defect site or in the neighbourhood of a molecule attached to a solid surface. Many other applications can be found in Ref. Its purpose is to perform quantum Monte Carlo electronic structure calculations for finite and periodic systems. Generality in this sense means that one ought to be able to create a trial wave function for any system, expanded in any of a variety of different b as is sets, and use it as input to a CASINO QMC calculation.

Farid and Needs [70], and references therein. Zeropoint energy corrections of 0. Maintaining these interfaces as codes evolve, and persuading their owners that this is a good idea in the first place, is a difficult and sometimes frustrating t as k. Towler: The quantum Monte Carlo method — Spin-polarized systems such as magnetic solids may be treated, as can systems with non-collinear spins albeit for a restricted set of c as es.

And from a computational point of view, one may also note that: — The source code is written in strict compliance with the Fortran90 standard using modern s of tware design techniques. It contains a self-documenting help system and comes with a helpful manual and examples.

Installed MPI libraries are not required on single processor machines and the code should compile and run out of the box on most machines. The speed of the code scales essentially linearly with the number of processors on a parallel computer. Various different versions of this were able to treat fcc solids, single atoms and the homogeneous electron g as. By the late s it w as clear that a modern general code capable of treating arbitrary systems e.

At that time, a user-friendly general publically available code did not exist, at le as t for periodic systems, and it w as felt to be a good thing to create one to allow other researchers to join in the fun. Some routines from the old code were retained, translated and reused, although most were gradually replaced. The code continues to be actively developed. I have tried to make the c as e that QMC is a useful addition to the toolbox of the computational electronic structure theorist.

The Top Ten list contained the following twelve re as ons: 1. We need forces, dummy! Try getting O 2 to bind at the variational level. How many graduate students lives have been lost optimizing wavefunctions? It is hard to get 0. Most chemical problems have more than 50 electrons.

How many spectra have you seen computed by QMC? QMC is only exact for energies. Multiple determinants. After all, electrons are fermions. Electrons move. Who programs anyway? This apparently first appeared on the web in , so it might be worth examining whether any progress h as been made in these are as in the l as t ten years. Of course it is true that for QMC to be considered a general method, one ought to be able to calculate forces i. In fact almost all QMC calculations up to the present time have been done within the Born—Oppenheimer approximation.

The nuclear positions are thus fixed during the calculation and the wave function depends parametrically on the nuclear coordinates. The fixed nuclear positions are normally taken from geometry optimizations done with alternative methods such as DFT, on the principle that DFT is more reliable for geometries than for total energy differences. Calculating forces using a stoch as tic algorithm is a difficult thing to do. A straightforward application of the Hellmann—Feynman theorem where the force is given by the gradient of the potential energy surface with respect to nuclear positions leads to estimators with a very large variance.

While a convincing general algorithm h as yet to be demonstrated for QMC calculations, some progress h as been made. The literature contains a variety of interesting contributions to this problem, which may be roughly cl as sified into three groups: — finite differences using correlated sampling techniques which take advantages of correlations between statistical samples to reduce the overall statistical error, e.

This is not normally useful in QMC as the Hellmann—Feynman estimator at le as t with bare nuclei h as an infinite variance as sociated with it. Furthermore, the Hellmann—Feynman expression does not give the exact derivative of the DMC energy if the nodal surface is not exact and depends on the nuclear positions. This is due to an additional nodal term rising from the action of the kinetic-energy operator on the discontinuity in the derivative of the wave function at inexact nodal surfaces [82].

However, some progress h as been made and one h as to be re as onably optimistic that a better general method for calculating forces will be devised in the near future. However, one can choose not to be overly concerned with QMC calculations done at the variational level. The binding energy of the oxygen molecule comes out very accurately in DMC [85, 86].

To give a feel for the time scale involved in optimizing wave functions, I can tell you about the weekend recently when I added the entire G set [87, 88] to the examples included with the CASINO distribution. This is a standard set of 55 molecules with various experimentally well-characterized properties intended for benchmarking of different quantum chemistry methods see e.

Grossman h as published the results of DMC calculations of these molecules using pseudopotentials [85], while we are doing the same with all-electron calculations [86]. It took a little over three days using only a few singleprocessor workstations to create all 55 sets of example files from scratch including optimizing the J as - www. Towler: The quantum Monte Carlo method trow factors for each molecule.

While if one concentrated very hard on each individual c as e one might be able to pull a little more energy out of a VMC simulation, the optimized J as trow factors are all perfectly good enough to be used as input to DMC simulations. I suggest that the process is sufficiently automated these days that graduate students are better employed elsewhere; certainly we have not suffered any fatalities here in Cambridge.

With modern computers and efficient computer codes there are a great many systems where one can get sufficient accuracy in a re as onable time. Obviously this becomes incre as ingly difficult for heavier atoms and large systems, but as discussed previously, satisfying the electron-nuclear cusp condition accurately in all-electron calculations or using pseudopotentials helps a lot.

QMC calculations for several thousand electrons per simulation cell have been published, and this number will only incre as e with the new improved scaling techniques currently being introduced, and with the incre as ing power of available computational hardware. Such pseudopotentials seem to work best for sp-bonded systems, and it is not clear that particularly good results can be obtained in systems containing, for example, transition elements.

As previously explained, our group h as developed an on-line periodic table containing a new set of smooth non-divergent Dirac—Hartree—Fock pseudopotentials which seem to be particularly satisfactory for QMC calculations [62, 63].

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This parameter can but worth reading : x11vnc -storepasswd x11vnc -forever -repeat. Read more in silicone that leaves. Comments: I love if your not you can remove by the person who crossed him.Superconductivity is one of the most fascinating chapters of modern physics. It has been a continuous source of inspiration for different realms of physics and has shown a tremendous capacity of cross-fertilization, to say nothing of its numerous technological applications. Before giving a more accurate definition of this phenomenon let us however briefly sketch the historical path leading to it.

Two were the main steps in the disc World Scientific Publishing Company, , pages All living organisms consist of soft matter. For this reason alone, it is important to be able to understand and predict the structural and dynamical properties of soft materials such as polymers, surfactants, colloids, granular matter and liquids crystals. To achieve a better understanding of soft matter, three different approaches have to be integrated: experiment, theory and simulation. Kohanoff J.

Cambridge University Press, , Pages: Electronic structure problems are studied in condensed matter physics and theoretical chemistry to provide important insights into the properties of matter. This graduate textbook describes the main theoretical approaches and computational techniques, from the simplest approximations to the most sophisticated methods. It starts with a detailed description of the various theoretical approaches to calc Lautrup B.

Offering a modern approach to this most classical of subjects, Physics of Continuous Matter is first and foremost an introduction to the basic concepts and phenomenology of continuous systems, and the derivations of the equations of continuum mechanics from Newtonian mechanics. Although many examples, particularly in the earlier chapters, are taken from geophysics and astrophysics, the aut Martienssen W.

Springer, The data, encapsulated in over tables and illustrations, have been selected and extracted primarily from the extensive high-quality data collection Landolt-B? Patterson J. Solid-State Physics. ISBN DOI Crystal Binding and Structure. Lattice Vibrations and Thermal Properties. A common example is crystalline solids , which break continuous translational symmetry.

Other examples include magnetized ferromagnets , which break rotational symmetry , and more exotic states such as the ground state of a BCS superconductor , that breaks U 1 phase rotational symmetry. Goldstone's theorem in quantum field theory states that in a system with broken continuous symmetry, there may exist excitations with arbitrarily low energy, called the Goldstone bosons.

For example, in crystalline solids, these correspond to phonons , which are quantized versions of lattice vibrations. Phase transition refers to the change of phase of a system, which is brought about by change in an external parameter such as temperature. Classical phase transition occurs at finite temperature when the order of the system was destroyed.

For example, when ice melts and becomes water, the ordered crystal structure is destroyed. In quantum phase transitions , the temperature is set to absolute zero , and the non-thermal control parameter, such as pressure or magnetic field, causes the phase transitions when order is destroyed by quantum fluctuations originating from the Heisenberg uncertainty principle.

Here, the different quantum phases of the system refer to distinct ground states of the Hamiltonian matrix. Understanding the behavior of quantum phase transition is important in the difficult tasks of explaining the properties of rare-earth magnetic insulators, high-temperature superconductors, and other substances. Two classes of phase transitions occur: first-order transitions and second-order or continuous transitions.

For the latter, the two phases involved do not co-exist at the transition temperature, also called the critical point. Near the critical point, systems undergo critical behavior, wherein several of their properties such as correlation length , specific heat , and magnetic susceptibility diverge exponentially.

The simplest theory that can describe continuous phase transitions is the Ginzburg—Landau theory , which works in the so-called mean-field approximation. However, it can only roughly explain continuous phase transition for ferroelectrics and type I superconductors which involves long range microscopic interactions.

For other types of systems that involves short range interactions near the critical point, a better theory is needed. Near the critical point, the fluctuations happen over broad range of size scales while the feature of the whole system is scale invariant. Renormalization group methods successively average out the shortest wavelength fluctuations in stages while retaining their effects into the next stage.

Thus, the changes of a physical system as viewed at different size scales can be investigated systematically. The methods, together with powerful computer simulation, contribute greatly to the explanation of the critical phenomena associated with continuous phase transition. Experimental condensed matter physics involves the use of experimental probes to try to discover new properties of materials.

Such probes include effects of electric and magnetic fields , measuring response functions , transport properties and thermometry. Several condensed matter experiments involve scattering of an experimental probe, such as X-ray , optical photons , neutrons , etc. The choice of scattering probe depends on the observation energy scale of interest.

Visible light has energy on the scale of 1 electron volt eV and is used as a scattering probe to measure variations in material properties such as dielectric constant and refractive index. X-rays have energies of the order of 10 keV and hence are able to probe atomic length scales, and are used to measure variations in electron charge density.

Neutrons can also probe atomic length scales and are used to study scattering off nuclei and electron spins and magnetization as neutrons have spin but no charge. Coulomb and Mott scattering measurements can be made by using electron beams as scattering probes. In experimental condensed matter physics, external magnetic fields act as thermodynamic variables that control the state, phase transitions and properties of material systems. NMR experiments can be made in magnetic fields with strengths up to 60 Tesla.

Higher magnetic fields can improve the quality of NMR measurement data. The local structure , the structure of the nearest neighbour atoms, of condensed matter can be investigated with methods of nuclear spectroscopy , which are very sensitive to small changes. Using specific and radioactive nuclei , the nucleus becomes the probe that interacts with its surrounding electric and magnetic fields hyperfine interactions. The methods are suitable to study defects, diffusion, phase change, magnetism.

Common methods are e. Ultracold atom trapping in optical lattices is an experimental tool commonly used in condensed matter physics, and in atomic, molecular, and optical physics. The method involves using optical lasers to form an interference pattern , which acts as a lattice , in which ions or atoms can be placed at very low temperatures. Cold atoms in optical lattices are used as quantum simulators , that is, they act as controllable systems that can model behavior of more complicated systems, such as frustrated magnets.

In , a gas of rubidium atoms cooled down to a temperature of nK was used to experimentally realize the Bose—Einstein condensate , a novel state of matter originally predicted by S. Bose and Albert Einstein , wherein a large number of atoms occupy one quantum state. Research in condensed matter physics [40] [72] has given rise to several device applications, such as the development of the semiconductor transistor , [3] laser technology, [58] and several phenomena studied in the context of nanotechnology.

He and his team developed multiple molecular machines such as molecular car , molecular windmill and many more. In quantum computation , information is represented by quantum bits, or qubits. The qubits may decohere quickly before useful computation is completed. This serious problem must be solved before quantum computing may be realized.

To solve this problem, several promising approaches are proposed in condensed matter physics, including Josephson junction qubits, spintronic qubits using the spin orientation of magnetic materials, or the topological non-Abelian anyons from fractional quantum Hall effect states.

Condensed matter physics also has important uses for biophysics , for example, the experimental method of magnetic resonance imaging , which is widely used in medical diagnosis. From Wikipedia, the free encyclopedia.

Branch of physics dealing with a property of matter. States of matter. Phase phenomena. Electronic phases. Electronic phenomena. Magnetic phases. Soft matter. Main article: Emergence. Main article: Electronic band structure. Main article: Symmetry breaking. Main article: Phase transition. Further information: Scattering. Main article: Optical lattice. Soft matter Green—Kubo relations — Equation relating transport coefficients to correlation functions Green's function many-body theory Materials science — Interdisciplinary field which studies the discovery and design of new materials Nuclear spectroscopy Comparison of software for molecular mechanics modeling Transparent materials Orbital magnetization Symmetry in quantum mechanics — Properties underlying modern physics Mesoscopic physics — Subdiscipline of condensed matter physics that deals with materials of an intermediate length.

Physicists Eugene Wigner and Hillard Bell Huntington predicted in [15] that a state metallic hydrogen exists at sufficiently high pressures over 25 GPa , but this has not yet been observed. Physics Today Jobs. Archived from the original on Retrieved American Physical Society.

Retrieved 27 March Physical Review Letters. Bibcode : PhRvL. PMID Retrieved 31 March Reviews of Modern Physics. Bibcode : RvMPS.. Archived from the original PDF on 25 August Retrieved 1 June Douglas 6 October Princeton University Press. ISBN Department of Physics. Princeton University. November World Scientific Newsletter. CRC Press. Retrieved 20 April Physics in Perspective.

Bibcode : PhP S2CID Kinetic Theory of Liquids. Oxford University Press. Archived from the original PDF on 17 November Retrieved 7 April The collected works of Sir Humphry Davy: Vol. Journal of Physics. Bibcode : JPhCS. Bibcode : Natur. Elements of Physical Chemistry. Introduction to Solid State Physics. Physics Today. Bibcode : PhT Moments of Discovery. American Institute of Physics. Archived from the original on 15 May Retrieved 13 June Modern Physics Letters B.

Bibcode : MPLB Historical introduction PDF. International Tables for Crystallography. CiteSeerX Archived from the original PDF on American Journal of Mathematics. JSTOR Quantum Mechanics: Nonrelativistic Theory.

Pergamon Press. The Theory of Magnetism Made Simple. World Scientific. Differential Models of Hysteresis. Bibcode : AnHP The University of Chicago. Introduction to Many Body Physics.

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