CASINO manual - Theory of Condensed Matter

USE GJASTROW (Logical) If set to T, the general Jastrow factor (under development) will be
used. This Jastrow factor has to be defined in a parameters.casl file. The default is F.
USE GPCC (Logical) If this is set to T then short-ranged functions will be added to the orbitals to
ensure that the Kato cusp conditions are satisfied.
USE JASTROW (Logical) Use a wave function of the Slater-Jastrow form, where the Jastrow
factor exp(J) is an optimizable object that multiplies the determinant part in order to introduce
correlations in the system. The Jastrow factor is read from the ‘JASTROW’ block in
correlation.data (see Sec. 7.4.2). The form of casino’s Jastrow factor is described in Sec. 22.
If use jastrow is F then the Slater wave function will not be multiplied by the Jastrow factor.
USE ORBMODS (Logical) If use orbmods is set to T then the orbital-modification block in
correlation.data will be read, and the modification functions will be added to the numerical
atomic orbitals. This only applies if atom basis type is set to ‘numerical’, ‘gaussian’ or ‘slatertype’.
See Secs. 7.4.5 and 7.4.6. To optimize the corresponding parameters, use opt orbitals.
USE TMOVE (Logical If use tmove is T then the Casula nonlocal pseudopotential scheme [20]
will be used in DMC. So-called ‘T -moves’ will be performed in order to give a DMC energy
that is greater than or equal to the ground-state energy. This violates the detailed-balance
principle at finite time steps, but greatly improves the stability of the DMC algorithm when
nonlocal pseudopotentials are used. The advantages of T-moves are that they restores the
variational principle and prevent population explosions; the disadvantages of T-moves are that
the magnitude of the locality approximation is generally larger (although always positive), the
time-step bias is worse, and they require an enormous amount of memory in systems with large
numbers of particles. Because of these disadvantages, the default of use tmove is F and we
tend *not* to use them unless we face stability issues.
VIRTUAL NCONFIG, VIRTUAL NNODES, VIRTUAL NODE (Integers) Number of
configurations, number of processors and node number in virtual parallel variance minimization.
These parameters are not to be set manually.
VM E GUESS (Physical) If vm use e guess is T then vm e guess should be supplied as an
estimate of the ground-state energy. (Note that energy units should be supplied.) See Sec. 25.1.
VM FILTER (Logical) This keyword activates filtering of configurations in variance minimization
by making the weights (artificially) energy-dependent, i.e., W i = W (|E i − E ave |). This method
uses two parameters: vm filter thres and vm filter width. See Sec. 25.1.
VM FILTER THRES, VM FILTER WIDTH (Real) When limiting outlying configurations in
variance minimization (by setting the vm filter flag to T), the maximum deviation from the average
energy at which the (artificial) weight of a configuration W i = W (|E i −E ave |) is kept equal
to unity is vm filter thres times the square root of the unreweighted variance. Outside this
limit, the weight is brought to zero using a gaussian of width vm filter width times the square
root of the unreweighted variance. By default, vm filter thres is 4.0 and vm filter width is
2.0. See Sec. 25.1.
VM FORGIVING (Logical) Do not whinge about calculated configuration energies not agreeing
with those read in. Recommended to be set to F.
VM LINJAS ITS (Integer) vm linjas its specifies the maximum number of iterations to be performed
if vm linjas method is ‘CG’, ‘SD’, ‘BFGS’, ‘CG MC’, ‘BFGS MC’ or ‘GN MC’. If
vm linjas method is ‘MC’, ‘LM’, ‘CG MC’, ‘BFGS MC’, or ‘GN MC’ then it (also) specifies
the number of line minimizations to be performed. See Sec. 25.2. Setting vm linjas its to 0
gives default behaviour, which is usually adequate.
VM LINJAS METHOD (Text) vm linjas method specifies the method used to minimize the
quartic least-squares function in the varmin-linjas optimization scheme. vm linjas method
should be one of: ‘CG’ (conjugate gradients), ‘MC’ (Monte Carlo), ‘LM’ (line minimization),
‘SD’ (steepest descents), ‘BFGS’ (Broyden-Fletcher-Goldfarb-Shanno), ‘BFGS MC’ (BFGS and
Monte Carlo), ‘CG MC’ (conjugate gradients and Monte Carlo), ‘GN’ (Gauss-Newton) or
‘GN MC’ (Gauss-Newton and Monte Carlo). (See Sec. 25.2). ‘BFGS’ is the default method,
although in case of difficulty, it is worth trying ‘GN’.
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