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This is a good question. In general, it often may not matter too much, but as with all aspects of numerical convergence, the only way for you to know for sure is to investigate. The concise answer is that in most cases, you can probably feel quite comfortable using the starting geometry for your convergence tests if: 1) the structure is reasonable (e.g. it's ...

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Here is the simple bash script for testing kpoint energy convergence for VASP you can use same logic in python but you can also use this. for i in seq 1 1 5 # change the range needed do cat <<EOF >KPOINTS MONK #header file 0 M $i$i $i 0 0 0 EOF mkdir$i cp INCAR $i/ cp POSCAR$i/ cp POTCAR $i/ cp KPOINTS$i/ cd $i vasp-5.4.4 # This is vasp run ... 14 There is an example in the pyiron documentation to calculate energy volume curves: https://pyiron.readthedocs.io/en/latest/source/notebooks/energy_volume_curve.html And the corresponding jupyter notebook is available in the pyiron repository: https://github.com/pyiron/pyiron_atomistics/blob/master/notebooks/energy_volume_curve.ipynb To adjust the energy ... 12 As you might expect, the answer is: it depends. It really boils down to how different the two systems are. There are obvious cases where the settings will not transfer between two systems. The trivial example (at least for plane-wave, periodic DFT) is to consider System A as having the same elements as System B, but the former is a$10\times10\times10$unit ... 12 Let's just remind ourselves what k point sampling is. It is used to approximate the integral of a function over the Brillouin zone by replacing the integral with a sum over a finite number of points: $$\int_{BZ} F({\bf k})d{\bf k} \approx \sum_j w_j F({\bf k_j})$$ Now this is an approximation, and for any relatively sane function the error in this ... 11 The question you should be asking yourself is: what properties do I want to compute? You, naturally, want to make sure that property is converged. For instance, if you wish to compute the band gap, then it makes more sense to confirm that your band gap is converged with your given settings. The band gap could very well be more (or less) sensitive to a given ... 11 If you have already obtained satisfactory convergence with a (relatively) sparse k-point grid, there is no motivation to go for a denser grid. So if you have already achieved convergence with 12x12x4, there's no need to go to 12x12x12. If you are talking about graphene, which is 2-D, there is no need to sample points along the out-of-plane direction. There ... 11 Short answer: Yes, doubling the volume of the simulation cell you will be able to effectively sample a finer$\mathbf{k}$-point grid to calculate the dielectric function. However, the calculation will be more expensive than simply increasing the number of$\mathbf{k}$-points directly in the primitive cell calculation. Longer answer: In reciprocal space, the ... 10 In brief, it will influence the electronic energy and thereby all properties derived from that. Too small a smearing width and you might have trouble converging the self-consistent field. Too large and the extrapolation back to 0 K from the fictitious finite temperature will be less accurate. Depending on the smearing method (e.g. Gaussian smearing), you can ... 10 The short answer is "yes" you should check, however this is not as arduous as it might appear at first. Your initial convergence calculations will be fairly exhaustive, sweeping over a large range of basis set size, Brillouin zone sampling etc. but when you change exchange-correlation functional you only need to check that your previously determined ... 10 This is the script I wrote for convergence testing in Quantum ESPRESSO using Bash and Python. According to Stefaan Cottenier, hydrostatic pressure on a unit cell is the property tested for convergence because it is quite sensitive to numerical precision. If this property is converged with respect to the k-mesh, many other less sensitive properties will be ... 10 When using a method like density functional theory we have to consider the accuracy of (i) the numerical solution of the problem and (ii) the physical model we use. The convergence you are refering to is numerical (i.e. how many$\mathbf{k}$-points to include in the numerical approximation of replacing an integral over the Brillouin zone with a discrete sum).... 10 Andrew's answer covers a lot of other considerations but not some useful additional ones in the case of relaxing unit cell parameters in solids. If the unit cell is being optimized in addition to atomic positions, the cell stress converges more slowly with respect to energy cutoff and k-points compared to forces and total energy. A good way to check this is ... 10 On the mathematical side, there exists no algorithm that estimates the time required to converge a property within any predefined accuracy, however low the accuracy is. The reason is simple. Suppose you perform a series of simulations of a liquid, with successively low temperature. When you are above the freezing point of the liquid, your simulation probably ... 10 tl;dr: simulate for greater than N*period of oscillation To understand how I arrived at this answer consider that properties can be written as a function of the partition function Q. For example, the average value of the volume will be${\displaystyle \langle V\rangle =\pm {\frac {\partial \ln Q}{\partial \beta P}}.}$The partition function for N identical ... 9 There is no energy cut off test for calculations that employ atomic basis sets, in general: the calculation is well-defined with just the atomic basis set. For comparison, the Gaussian-basis PySCF program implements four ways to compute the Coulomb interactions in crystalline systems: Gaussian-basis density fitting mixed Gaussian-plane wave density fitting ... 9 This is an excellent question! The reality is complicated even in LCAO calculations: every code has different defaults, which also depend on the run type. It seems that older LCAO codes simultaneously look at the convergence of the energy, and of the density matrix. Looking only at the change in energy is really bad behavior, since it doesn't tell you ... 9 You should converge each calculation with respect to all the parameters; however, some parameters are relatively transferable between related systems. Let's consider the two main ones in a condensed-phase simulation: Size of basis set Brillouin zone sampling ("k-points") (Only relevant for periodic systems) The basis set is largely dependent on the ... 9 I agree with the answer provided by Xivi76. I just wanted to add that some codes have a very nice functionality that facilitates convergence: rather than explicitly writing out a$\mathbf{k}$-point grid$n_1\times n_2\times n_3$, in which in principle you have to converge three values; you can instead specify a$\mathbf{k}$-point spacing or density, in which ... 7 As far as my knowledge this a relationship between k-points and lattice constants values. I'll give you an example of a layered hexagonal material WS2: Lattice constants : a=3.17 b=3.17 c=12.41 ; so c/a = 12.41/3.17=3.91 K-points : If I chose kx=12, ky will be equal to 12 but kz should be equal to an integer close to the kx divided by c/a. I mean kz=12/3.91=... 7 The hydrostatic pressure on a given unit cell is an experimentally observable property very sensitive to numerical precision. If this property is converged many other less sensitive properties will be converged too. Hence checking for convergence of pressure will be a better bet to make sure that other properties are also converged Reference Source 7 The KPOINTS have an inverse relationship with the real space lattice vectors. In either way, you won't go wrong. I would prefer to test it using 1x1x2, 1x1x3, 1x1x4, etc. This is simply because your c axis is much smaller than the a and b axis. However, you might also want to explore the other alternatives such as 2x2x3, 2x2x4, etc. Since it is not ... 6 The best strategy when performing convergence tests is to directly converge the quantity you are interested in. This "quantity" can be a straight-forward physical property, like the band gap of a material, or a composite (for lack of a better word) property. In your case, you are interested in comparing the electronic density of states (DOS) ... 6 The k-point discretization has the same meaning in plane wave codes as LCAO based codes. In fact, it has the same meaning in all DFT related codes. It defines the integration of the Brillouin zone. You want the integration to be good enough to capture the relevant physics (e.g. graphene with k-point sampling touching the Dirac point vs Gamma-only) but also ... 6 I do generally follow the follwoing: Keep basic tags EDIFF, EDIFF to 1E-07 and 1E-06 (or -0.005) generally. i do use defaults if i want to do a faster run just for checking. and i dont know how can we preempt or know the potential surface is flat or not, as mentioned by Rosen. As in his answer said"...However, if the potential energy surface is flat, ... 6 I don't know if I can give you a definitive response, but I would like to give you some clues based on my experience working with DFT, with Quantum ESPRESSO to be more specific. As a thumb rule, I guess some parameters should be converged a priori. Among them are the energy cutoff and k-points sampling. Those are significant parameters to take care of. ... 6 I will try to give the most practical answer, the reality of "is this converged" is that you cannot know without checking by going past it. You say that you would like to save time doing these calculations, but the worst loss of time is sometimes the loss of your own human time as you get confusing results later. I am unsure how you came to the ... 6 I wasn't going to answer, because there is no answer, but, anyways... First: common practice is... Common practice is to frequently measure the property you are after, as well as others, such as internal energy, density and make a plot. The property will fluctuate but eventually reach an equilibrium that is easy for the eye to spot on a plot. Or, use the ... 5 You can make a convergence test to obtain reasonable results. Usually, for k-sampling and energy cutoff, you can take some values from experiences (of course, you can also make convergence tests). (a) ENCUT=largest ENMAX on the POTCAR file$\times\$ 1.5 (b) KPOINTS: you can using VASPKIT to generate KPOINTS when you prepare a POSCAR. ========================...

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The SCF=QC keyword in Gaussian actually pertains to the choice of algorithm on how find the solution in the SCF procedure while SCF=Tight is an option related to SCF cycle convergence. Gaussian SCF criterion depends on density matrix changes, consequently in energy. So using QC algorithm should not cost accuracy if the convergence criterion is the same. If ...

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