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

[Tianhua Wang]

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
[image: SnO2.jpg]

  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.

[image: Density.jpg]
*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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