If the site file is

  ATOM=La         POS=  0.5000000   0.5000000  -0.4807290
  ATOM=La         POS= -0.5000000  -0.5000000   0.4807290
  ATOM=Cu         POS=  0.0000000   0.0000000   0.0000000
  ATOM=O          POS=  0.0000000   0.5000000   0.0000000
  ATOM=O          POS=  0.0000000   0.0000000   0.6320273
  ATOM=O          POS= -0.5000000   0.0000000   0.0000000
  ATOM=O          POS=  0.0000000   0.0000000  -0.6320273

then iatom=3.

Basic parameters (U, J, nf0 and beta)

Among the possible parameters are U and J, defining respectively the Hubbard in-site interaction and the Hund’s coupling constant in eV. Warning: The same value of J has to be passed to atom_d.py as well.

The variable nf0 is the nominal occupancy of the correlated orbitals (e.g. nf0 9 for $Cu$, nf0 8 for Ni).

Finally beta fixes the inverse temperature in eV$^{-1}$.

Warning: Don’t forget to be consistent when you run atom_d.py (which wants J as an input) and lmfdmft (for which the flag --ldadc= has to be consistently defined as U(nf0-0.5)-J(nf0-1)*0.5).

Setting the number of sampled frequencies (nom and nomD)

The CTQMC solver gives a very accurate description of the self-energy in the low frequency range (for Matsubara’s frequencies close to 0), but it becomes too noisy at high frequencies.

Let $N$ be the total number of Matsubara’s frequencies. This number has been defined through NOMEGA in the ctrl file during the lmfdmft run. Only the first nom frequencies are actually sampled by the CTQMC solver, while the other points are obtained analytically through the approximated Hubbard 1 solver (high-frequency tail).

Excessively low values of nom will miss important features of the self-energy (e.g. a convex point in $\text{Im}[\Sigma(i\omega_n)]$), while too high values will give excessively noisy results. As a rule-of-thumb, a good initial guess is nom $\approx 3$ beta, but this value has usually to be adjusted during the DMFT loop. Some examples of how the $\text{Im}[\Sigma(i\omega_n)]$ looks like at different values of nom are given in the figures below.

Setting the number of Monte Carlo steps (M and warmup)

The higher is the number of Monte Carlo steps, the lower the noise in the QMC calculation. The parameter M defines the number of MC steps per core. Reliable calculations easily require at least 500 millions of steps in total. For instance, if you’re running on 10 cores, you can set M 50000000. You can judge the quality of your sampling by looking at the file histogram.dat. The closer it looks to a Gaussian distribution, the better is the sampling.

Warning: The variable M should be set keeping in mind that the higher it is, the longer the calculation. This is crucial when running on public clusters, where the elapsed time is computed per core. Too high values of M may consume your accounted hours very quickly! Moreover remember that you are supposed to broaden the output at each iteration, so you don’t actually need very clean Sig.out

During the first warmup steps results are not accumulated, as it is normal on Monte Carlo procedures. This gives the ‘time’ to the algorithm to thermalise before the significative sampling. You can set warmup=M/1000.

Setting the cutoff expansion order (Nmax)

The variable Nmax defines the highest order accounted for in the hybrdization expansion. If you have chosen an excessively low values of Nmax, the histogram.dat file will be cut and $\text{Im}[\Sigma(i\omega_n)]$ will look weird, as shown below.

You should chose Nmax high enough for the Gaussian distribution of histogram.dat to be comfortably displayed. However note that the higher Nmax the longer the calculation, so chose values just above the higher Guassian tail.

Warning: the value of beta affects the number Nmax, so calculations on the same material at different temperatures will require different Nmax. At low beta, the Gaussian distribution is sharper and centered on lower order terms, as shown below. Therefore lower beta require lower Nmax.

Connecting the tail (sderiv and aom)

The connection between the QMC part and the Hubbard 1 part is done with a straight line starting at frequency number nom+1 and running until it intersect the Hubbard 1 self-energy. You can see it by comparing the Sig.out file with the s_hb1.dat (Hubbrad 1 only).

The two variables controlling the connection are sderiv, which is related to the angle of the straight line, and aom related to its starting point.

The straight line connecting the low- and the high-frequncy region can easily give rise to unwanted kinks. To broaden Sig.out is necessary to smooth these kinks out as well.

Impurity levels (Ed and mu)

The impurity levels reported at the fourth line of Eimp.inp enters in the PARAMS as the variable Ed. The variable mu is set as the first entry of the Ed with opposite sign.

Warning: These two variables change at every iteration so you have to constantly update their value throughout the DMFT loop.

In this case usually one or more channels of the self-energy are very noisy. One has to run for longer times, or use the status files to restart the calculation many times until only one peaks dominate and the histogram looks like a Gaussian. An example is given in the boxes below.

They can be used basically in two ways.

• If you are performing iteration N, you should copy the status files from iteration N-1 to speed up the convergence of the calculation.
• If you realise that in one ctqmc run, you haven’t achieved a good sampling (e.g. M too low, or close to phase transition), than you can run again the calculation without losing the effort already done. When restarted, the ctqmc solver will automatically read the status files and retrive their information.

Warning: Since there is one status file per core, you must pay attention to run on as many cores as status files you have. It should be safe (but not recommended) to run with a smaller number of cores, while running on more cores than status files gives wrong results.