[CP2K-user] [CP2K:14980] Problem about the analytical stress tensor for meta-GGA functionals in CP2K 8.1

[hut... at chem.uzh.ch]

Hi

it is difficult to say where the problem is. You can check the
correctness of the analytic stress tensor by running a debug
run. This will compare the analytic stress to a finite difference
calculation. You could do this for your SnO2 example.
We do this for other examples and the tests are passed.

Besides that, it might be also a problem of pseudopotential, basis set
and/or cutoff. In principle you have to check all of this in 
independent runs. For this it would be best to have an independent
result to compare to.
Another line of tests could be to use another MGGA (like TPSS)
to see if you get consistent results.

best regards

Juerg Hutter

--------------------------------------------------------------
Juerg Hutter                         Phone : ++41 44 635 4491
Institut für Chemie C                FAX   : ++41 44 635 6838
Universität Zürich                   E-mail: hut... at chem.uzh.ch
Winterthurerstrasse 190
CH-8057 Zürich, Switzerland
---------------------------------------------------------------

-----cp... at googlegroups.com wrote: -----
To: "cp2k" <cp... at googlegroups.com>
From: "Tianhua Wang" 
Sent by: cp... at googlegroups.com
Date: 03/22/2021 09:56AM
Subject: [CP2K:14980] Problem about the analytical stress tensor for meta-GGA functionals in CP2K 8.1

Dear CP2K developers,
  Thank you very much for developing the analytical stress tensor for meta-GGA functionals in CP2K 8.1. By using the cp2k-8.1-Linux-x86_64.ssmp downloaded from the GitHub project page of CP2K, I did two series of calculations in which the analytical stress tensor for SCAN functional was used. However, the results indicated that the calculations using SCAN functional underestimated volumes significantly, which was inconsistent with the good performance of SCAN functional reported in literatures. I am wondering if there could be something wrong with my operations or the analytical stress tensor for meta-GGA functionals in CP2K 8.1. The computational details and results are shown as below.
  Firstly, cell optimizations for cassiterite (SnO2) were performed at different computational levels, i.e., combining three different functionals (i.e., SCAN, LDA (PADE) and PBE) with DZVP/TZVP basis sets and GTH pseudopotentials. The model of cassiterite was a supercell consisting of 2×2×4 unit cells, and its initial structure was constructed based on the powder neutron diffraction data (Bolzan et al., 1997, Acta Cryst. B). The input file for the calculation at the SCAN/TZVP level is attached for reference. From the lattice parameters of optimized structures tabulated in Table 1, it can be seen that the calculations using SCAN functional underestimated volumes significantly, and the relative errors (compared with experimental values) produced by SCAN functional are even larger than those produced by PBE functional.

Table 1. Lattice parameters of optimized structures of cassiterite (SnO2). a


  Another series of calculations were the FPMD simulations of liquid water in isothermal-isobaric (NPT) ensemble at ambient conditions, in which the temperature of 330 K was used. The simulations were performed by using SCAN and PBE-D3, together with DZVP basis sets and GTH pseudopotentials. For simulations using SCAN, three kinds of DZVP basis sets and GTH pseudopotentials were performed, i.e., (1) DZVP-MOLOPT-SCAN-GTH basis sets and GTH-SCAN pseudopotentials from the GitHub project page of Prof. Jürg Hutter, (2) DZVP-MOLOPT-SR-GTH basis sets in the BASIS_MOLOPT file and GTH-SCAN pseudopotentials mentioned above, and (3) DZVP-MOLOPT-SR-GTH basis sets in the BASIS_MOLOPT file and GTH-PBE pseudopotentials in the GTH_POTENTIALS file. The input file for the calculation using SCAN functional with MOLOPT-SCAN-GTH basis sets and GTH-SCAN pseudopotentials (i.e., (1) mentioned above) is attached for reference. For the simulation using PBE-D3, DZVP-MOLOPT-SR-GTH basis sets in the BASIS_MOLOPT file and GTH-PBE pseudopotentials in the GTH_POTENTIALS file were employed. Each simulation was performed for 10.0 ps, following a classical MD simulation for SPC/E water in NVT ensemble for 10.0 ns. The running average densities derived from the last 8.0 ps of the simulations were shown in Figure 1. I know 10.0 ps is too short to derive a reasonable density, and the data in Figure 1 are far away from convergence. However, on the one hand, the computational efficiency of cp2k-8.1-Linux-x86_64.ssmp is relatively low, and it has taken a long time for these simulations; on the other hand, as shown in Figure 1, the densities derived from the simulations using SCAN functional are all larger than the density derived from the simulation using PBE-D3, which is similar with the phenomenon seen in the cell optimizations for cassiterite.


Figure 1. Running average densities as a function of simulation time.

Thank you for your time and I am looking forward to your reply.
Sincerely,
Tianhua Wang


  
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[attachment "330NPT-1bar_64H2O_SCAN+DZVP-SCAN_800Ry.inp" removed by Jürg Hutter/at/UZH]
[attachment "CELL_OPT_32SnO2_SCAN_TZVP_0bar.inp" removed by Jürg Hutter/at/UZH]