Present of year 2023

I wish everyone a Merry Christmas and a Happy New Year!

As I present, let me share the discovery of this year.

Ferdium is a program that combines all messengers in a single window! I tried to distinguish between work and life using different messengers for years. For work, I used Unfortunately, they decided to close all freemium accounts and raise the prices this year. So, I switched to other messengers and eventually mixed them up. Luckily, I found Ferdium! Just see my print screen – all messengers in one app:

Go to to get it.

By the way, Opera provides a similar functionality, but it does not have so many app in it. For example, it does not have Element.

Zotero + chatGPT via pdfGEAR

Some time ago (in 2023), I linked Zotero with chatGPT by creating an environment with paper-qa and pyzotero like this:
conda create -n Zotero
conda activate Zotero
conda install pip
pip install paper-qa
pip install pyzotero
pip install bs4

That worked but felt way too complicated … like I am not going to use it on a daily basis. It also reminded me the very first experience with the Meta AI in late 2022 (which everyone already forgot).

Here is a much simpler recipe:

  1. Install Zotero add-on from to enable opening with external pdf viewers.
  2. Install pdfGEAR as your default pdf viewer (external to Zotero).

See how it works on my YouTube channel:

DFT geometry optimizers

These are undeservedly less attention to optimizers than density functionals (concerning Jacob’s ladder). It is not even covered in the recent review: Best-Practice DFT Protocols for Basic Molecular Computational Chemistry. At the same time, in my current projects, the most resource-demanding was geometry optimization – the time spent on optimizing structures was much longer than a single-point calculation. Papers that introduce new (AI-based) optimizers promise significant speed-up. However, there are always some problems:

  1. The tested systems are different from my electrochemical interfaces.
  2. The code is not available or difficult to install.
  3. The code is outdated and contains bugs.
  4. Optimizers perform worse than the common ones, like QuasiNewton in ASE.

ASE wiki lists all internal and some external optimizers and provides their comparison. I have checked the most promising on a high-entropy alloy slab.

Observation 1. QuasiNewton outperforms all other optimizers. Point. I have run a standard GPAW/DFT/PBE/PW optimization with various optimizers:

Observation 2. Pre-optimizing the slab with a cheaper method does not reduce the number of optimization steps. I have preoptimized the geometry with TBLITE/DFTB/GFN1-xTB to continue with GPAW/DFT/PBE/PW. Preoptimization takes just some minutes and the obtained geometry looks similar to the DFT one but that does not reduce the number of DFT optimization steps.

OptimizerN steps*Time$N steps*#Total time#

Note * – the printed number of steps might different from the actuall number of calculations because each calculator has a different way of reporting that number.

Note $ – the time between the end of the first and last steps.

Note # – started from the TBLITE/DFTB/GFN1-xTB preoptimized geometry.

N.B! I have done my test only once in two runs: starting with and preoptized geometry. Runs were on similar nodes and all optimizations were done on the same node.

Conclusion. Do not believe in claims in articles advertizing new optimizers – Run your tests before using them.

A practical finding. The usual problem with calculations that require many optimization steps is that they need to fit into HPC time limits. On the restart, ASE usually rewrites the trajectory. Some optimizers (GPMin and AI-based) could benefit from reading the full trajectory. So, I started writing two trajectories and a restart file like this.

# Restarting
if os.path.exists(f'{name}_last.gpw') == True and os.stat(f'{name}_last.gpw').st_size > 0:
    atoms,calc = restart(f'{name}_last.gpw', txt=None)
    parprint(f'Restart from the gpw geometry.')
elif os.path.exists(f'{name}_full.traj') == True and os.stat(f'{name}_full.traj').st_size > 0:
    atoms = read(f'{name}_full.traj',-1)
    parprint(f'Restart with the traj geometry.')
    atoms = read(f'{name}')
    parprint(f'Start with the initial xyz geometry.')

# Optimizing
opt = QuasiNewton(atoms, trajectory=f'{name}.traj', logfile=f'{name}.log')
traj= Trajectory(f'{name}_full.traj', 'a', atoms)
opt.attach(traj.write, interval=1)
def writegpw():
opt.attach(writegpw, interval=1), steps=42)

Here are some details on the tests.

My for DFT calculations on 24 cores:

# Load modules
from ase import Atom, Atoms
from import add_adsorbate, fcc100, fcc110, fcc111, fcc211, molecule
from ase.calculators.mixing import SumCalculator
from ase.constraints import FixAtoms, FixedPlane, FixInternals
from import vdw_radii
from ase.db import connect
from import write, read
from ase.optimize import BFGS, GPMin, LBFGS, FIRE, QuasiNewton
from ase.parallel import parprint
from ase.units import Bohr
from bondmin import BondMin
from catlearn.optimize.mlmin import MLMin
from dftd4.ase import DFTD4
from gpaw import GPAW, PW, FermiDirac, PoissonSolver, Mixer, restart
from gpaw.dipole_correction import DipoleCorrection
from gpaw.external import ConstantElectricField
from gpaw.utilities import h2gpts
import numpy as np
import os

atoms = read('')
atoms.set_constraint([FixAtoms(indices=[atom.index for atom in atoms if atom.tag in [1,2]])])

# Set calculator
kwargs = dict(poissonsolver={'dipolelayer':'xy'},
              gpts=h2gpts(0.18, atoms.get_cell(), idiv=4),
calc = GPAW(**kwargs)

#atoms.calc = SumCalculator([DFTD4(method='RPBE'), calc])
#atoms.calc = calc

# Optimization paramters
maxf = 0.05

# Run optimization

# 2.A. Optimize structure using MLMin (CatLearn).
initial_mlmin = atoms.copy()
mlmin_opt = MLMin(initial_mlmin, trajectory='results_mlmin.traj'), kernel='SQE', full_output=True)

# 2.B Optimize using GPMin.
initial_gpmin = atoms.copy()
gpmin_opt = GPMin(initial_gpmin, trajectory='results_gpmin.traj', logfile='results_gpmin.log', update_hyperparams=True)

# 2.C Optimize using LBFGS.
initial_lbfgs = atoms.copy()
lbfgs_opt = LBFGS(initial_lbfgs, trajectory='results_lbfgs.traj', logfile='results_lbfgs.log')

# 2.D Optimize using FIRE.
initial_fire = atoms.copy()
fire_opt = FIRE(initial_fire, trajectory='results_fire.traj', logfile='results_fire.log')

# 2.E Optimize using QuasiNewton.
initial_qn = atoms.copy()
qn_opt = QuasiNewton(initial_qn, trajectory='results_qn.traj', logfile='results_qn.log')

# 2.F Optimize using BFGS.
initial_bfgs = atoms.copy()
bfgs_opt = LBFGS(initial_bfgs, trajectory='results_bfgs.traj', logfile='results_bfgs.log')

# 2.G. Optimize structure using BondMin.
initial_bondmin = atoms.copy()
bondmin_opt = BondMin(initial_bondmin, trajectory='results_bondmin.traj',logfile='results_bondmin.log')

# Summary of the results

fire_results = read('results_fire.traj', ':')
parprint('Number of function evaluations using FIRE:',

lbfgs_results = read('results_lbfgs.traj', ':')
parprint('Number of function evaluations using LBFGS:',

gpmin_results = read('results_gpmin.traj', ':')
parprint('Number of function evaluations using GPMin:',

bfgs_results = read('results_bfgs.traj', ':')
parprint('Number of function evaluations using BFGS:',

qn_results = read('results_qn.traj', ':')
parprint('Number of function evaluations using QN:',

catlearn_results = read('results_mlmin.traj', ':')
parprint('Number of function evaluations using MLMin:',

bondmin_results = read('results_bondmin.traj', ':')
parprint('Number of function evaluations using BondMin:',

Initial file:

Lattice="8.529357696932532 0.0 0.0 4.264678848466266 7.386640443507905 0.0 0.0 0.0 29.190908217261956" Properties=species:S:1:pos:R:3:tags:I:1 pbc="T T F"
Ir       0.00000000       1.62473838      10.00000000        5
Ru       2.81412943       1.62473838      10.00000000        5
Pt       5.62825885       1.62473838      10.00000000        5
Pd       1.40706471       4.06184595      10.00000000        5
Ag       4.22119414       4.06184595      10.00000000        5
Ag       7.03532356       4.06184595      10.00000000        5
Ag       2.81412943       6.49895353      10.00000000        5
Ru       5.62825885       6.49895353      10.00000000        5
Pt       8.44238828       6.49895353      10.00000000        5
Pt       0.00000000       0.00000000      12.29772705        4
Ag       2.81412943       0.00000000      12.29772705        4
Ru       5.62825885       0.00000000      12.29772705        4
Ru       1.40706471       2.43710757      12.29772705        4
Ir       4.22119414       2.43710757      12.29772705        4
Ag       7.03532356       2.43710757      12.29772705        4
Ag       2.81412943       4.87421514      12.29772705        4
Ir       5.62825885       4.87421514      12.29772705        4
Pd       8.44238828       4.87421514      12.29772705        4
Pd       1.40706471       0.81236919      14.59545411        3
Ir       4.22119414       0.81236919      14.59545411        3
Pt       7.03532356       0.81236919      14.59545411        3
Ag       2.81412943       3.24947676      14.59545411        3
Ir       5.62825885       3.24947676      14.59545411        3
Ir       8.44238828       3.24947676      14.59545411        3
Pd       4.22119414       5.68658433      14.59545411        3
Pt       7.03532356       5.68658433      14.59545411        3
Ag       9.84945299       5.68658433      14.59545411        3
Pd       0.00000000       1.62473838      16.89318116        2
Pd       2.81412943       1.62473838      16.89318116        2
Ag       5.62825885       1.62473838      16.89318116        2
Pt       1.40706471       4.06184595      16.89318116        2
Ag       4.22119414       4.06184595      16.89318116        2
Ag       7.03532356       4.06184595      16.89318116        2
Ru       2.81412943       6.49895353      16.89318116        2
Ru       5.62825885       6.49895353      16.89318116        2
Ru       8.44238828       6.49895353      16.89318116        2
Ir       0.00000000       0.00000000      19.19090822        1
Ag       2.81412943       0.00000000      19.19090822        1
Pt       5.62825885       0.00000000      19.19090822        1
Pd       1.40706471       2.43710757      19.19090822        1
Ag       4.22119414       2.43710757      19.19090822        1
Pd       7.03532356       2.43710757      19.19090822        1
Ag       2.81412943       4.87421514      19.19090822        1
Ru       5.62825885       4.87421514      19.19090822        1
Ir       8.44238828       4.87421514      19.19090822        1

My for DFTB calcualation with just one core. It takes some minutes but eventually crashes 🙁

# Load modules
from ase import Atom, Atoms
from import add_adsorbate, fcc100, fcc110, fcc111, fcc211, molecule
from ase.calculators.mixing import SumCalculator
from ase.constraints import FixAtoms, FixedPlane, FixInternals
from import vdw_radii
from ase.db import connect
from import write, read
from ase.optimize import BFGS, GPMin, LBFGS, FIRE, QuasiNewton
from ase.parallel import parprint
from ase.units import Bohr
from tblite.ase import TBLite
import numpy as np
import os


atoms = read('')
atoms.set_constraint([FixAtoms(indices=[atom.index for atom in atoms if atom.tag in [1,2]])])

# Set calculator
calc = TBLite(method="GFN1-xTB",accuracy=1000,electronic_temperature=300,max_iterations=300)
qn_opt = QuasiNewton(atoms, trajectory='results_qn.traj', logfile='results_qn.log', maxstep=0.1)

To compare structures I have used MDanalysis, which unfortunately does not work with ASE traj, so I prepared xyz-files with “ase convert -n -1 file.traj”

import MDAnalysis as mda
from MDAnalysis.analysis.rms import rmsd
import sys

def coord(file_name):
    file  = mda.Universe(f"{file_name}.xyz")
    atoms = file.select_atoms("index 1:9")
    return  atoms.positions.copy()


An instruction on installation of GPAW. TBLITE can be installed as “conda install -c conda-forge tblite”.

GPAW installation with pip

Between installation with conda and compilation of libraries, an intermediate path – installation of GPAW with pip – is a compromise for those who wish to text specific GPAW branches or packages.

For example, I wish to text self-interaction error correction (SIC) and evaluate Bader charges with pybader. Neither SIC nor pybader is compatible with the recent GPAW. Here is not to get a workable version.

# numba in pybader is not compatible with python 3.11, so create a conda environment with python 3.10
conda create -n gpaw-pip python=3.10 
conda activate gpaw-pip

conda install -c conda-forge libxc libvdwxc
conda install -c conda-forge ase
# ensure that you install the right openmpi (not external)
conda install -c conda-forge openmpi ucx
conda install -c conda-forge compilers
conda install -c conda-forge openblas scalapack
conda install -c conda-forge pytest
pip install pybader

# Get a developer version of GPAW with SIC
git clone -b dm_sic_mom_update
cd gpaw

# In the rewrite
fftw = True
scalapack = True
if scalapack:
    libraries += ['scalapack']

unset CC
python -m pip install -e .
gpaw info

k-points with kplib and gpaw

Choosing optimal k-points is a tricky task. In GPAW, one can set them manually, using size or density and following a rule of thumb:

calc = GPAW(kpts={'size': (4, 4, 4), 'gamma': True})
# or
calc = GPAW(kpts={'density': 2.5, 'gamma': True})

A rule of thumb for choosing the initial k-point sampling is, that the product, ka, between the number of k-points, k, in any direction, and the length of the basis vector in this direction, a, should be:

  • ka ~ 30 Å, for d band metals
  • ka ~ 25 Å, for simple metals
  • ka ~ 20 Å, for semiconductors
  • ka ~ 15 Å, for insulators

Remember that convergence in this parameter should always be checked.

The corresponding densities (ka/2π) are:

  • ka/2π ~ 4.8 Å, for d band metals
  • ka/2π ~ 4.0 Å, for simple metals
  • ka/2π ~ 3.2 Å, for semiconductors
  • ka/2π ~ 2.4 Å, for insulators

With the recent update, I can start using kplib (see paper) to choose the optimal generalized k-point grids. The main variable in kplib is min_distance, which is analogous to the density×2π. Read more about the min_distance at

Here is an example of my conda environment

conda create -n gpaw23 python=3.9
conda activate gpaw23
conda install -c conda-forge cxx-compiler
pip install kplib # from
conda install -c conda-forge gpaw

Here is a working example:

from ase import Atoms
from ase.parallel import parprint
from gpaw import GPAW, PW
from kpLib import get_kpoints
from import AseAtomsAdaptor

atoms = Atoms(cell=[[1.608145, -2.785389, 0.0], [1.608145, 2.785389, 0.0], [0.0, 0.0, 5.239962]],
              symbols=['Ga', 'Ga', 'N', 'N'],
              positions=[[ 1.608145  , -0.92846486,  2.61536983],
                         [ 1.608145  ,  0.92846486,  5.23535083],
                         [ 1.608145  , -0.92846486,  4.58957792],
                         [ 1.608145  ,  0.92846486,  1.96959692]],
structure = AseAtomsAdaptor.get_structure(atoms)
kpts_data = get_kpoints(structure, minDistance=30, include_gamma=False)
parprint("Found lattice with kplib: ")
parprint(f"Nominal kpts: {kpts_data['num_total_kpts']}")
parprint(f"Distinct kpts: {kpts_data['num_distinct_kpts']}")

atoms.calc = GPAW(xc='PBE',
                  symmetry={'point_group': True,
                            'time_reversal': True,
                            'symmorphic': False,
                            'tolerance': 1e-4},
energy = atoms.get_total_energy()

parprint(f"Total energy: {energy}")
parprint(f"kpts passed to GPAW: {len(atoms.calc.get_bz_k_points())}")
parprint(f"kpts in GPAW IBZ: {len(atoms.calc.get_ibz_k_points())}")

Installing GPAW with conda

[Updated on 20.04.2022, 15.04.2023, 10.06.2023, 03.10.2023, 04.06.2024]

In short, in a clean environment, everything should work with just five lines:


Initialize conda. If it is in the .bashch, source it. If not, source “PATHTOCONDA/miniconda3/etc/profile.d/”.

conda create --name gpaw -c conda-forge python=3.12
conda activate gpaw
conda install -c conda-forge openmpi ucx
conda install -c conda-forge gpaw=24.1.0=*openmpi*

For details, see the description below.

1. Install conda – software and environment management system.

Here is the official instruction:

On June 2024, run these:


If you wish to autostart conda, allow it to write to your .bashrc.

P.S. Here are good intros to conda:

N.B! If the locale is not set, add it to your .bashrc export


Without it python might give a segmentation fault (core dumped) error.

2. Create a conda virtual environment:

conda create --name gpaw -c conda-forge python=3.12

If needed, remove the environment as:

conda remove --name gpaw --all

You can check the available environments as:

conda env list

3. Activate the virtual environment.

conda activate gpaw

4. Install gpaw:

Ensure that no interfering modules and environments are loaded.

Purge modules by executing:

module purge

To check whether some code (like mpirun) has an alternative path, try:

which codename


codename --version

There should be no mpirun, ase, libxc, numpy, scipy, etc. Otherwise, the installation with conda will most probably fail due to conflicting paths.

4.1. It is safer to install using gpaw*.yml file from vliv/conda directory on FEND:

conda env create -f gpaw.yml

Note that there are many yml files with different versions of GPAW.

4.2. Pure installation is simple but might not work:

conda install -c conda-forge openmpi

conda install -c conda-forge gpaw=*=*openmpi*

In 2022, there were problems with openmpi. Downgrading to version 4.1.2 helped:

conda install -c conda-forge openmpi=4.1.2

You might wish to install ucx but be aware that there are many problems with it, e. g. depending on mlx version:

conda install -c conda-forge ucx

If you get an error about GLIBCXX, try upgrading gcc:

conda install -c conda-forge gcc=12.1.0

4.3. To quickly check the installation, run “gpaw -P 2 test” or “gpaw info”.

The installation might fail. In case you succeed, save the yml file as:

conda env export | grep -v "^prefix: " > gpaw.yml

Now you can use it to install gpaw as:

conda env create -f gpaw.yml

To properly test the installation install pytest and follow That might take hours.

conda install -c conda-forge pytest pytest-xdist 

5. If needed, install extra packages within your specific conda environment (gpaw).

To apply D4 dispersion correction:

conda install -c conda-forge dftd4 dftd4-python

To analyze trajectories:

conda install -c conda-forge mdanalysis

To analyze electronic density (some might not work):

pip install git+
pip install git+
pip install git+
pip install pybader
pip install cpmd-cube-tools
conda install -c conda-forge chargemol

To use catlearn:

pip install catlearn

To work with crystal symmetries:

conda install -c conda-forge spglib

Extra for visualization (matplotlib comes with ASE):

conda install -c conda-forge pandas seaborn bokeh jmol

To use notebooks (you might need to install firefox as well):

conda install -c conda-forge jupyterlab nodejs jupyter_contrib_nbextensions 

6. Run calculations by adding these lines to the submission script:

Note1: Check the path and change the USERNAME

Note2: Turn off ucx.

Note3: You may play with the number of openmp threads.

module purge
source "/groups/kemi/USERNAME/miniconda3/etc/profile.d/"
conda activate gpaw
export OMPI_MCA_pml="^ucx"
export OMPI_MCA_osc="^ucx"
mpirun gpaw python

Note4: Check an example in vliv/conda/sub directory.

7. Speeding-up calculations.

Add the “parallel” keyword to GPAW calculator:

parallel = {'augment_grids':True,'sl_auto':True},

For more options see For LCAO mode, try ELPA. See

parallel = {'augment_grids':True,'sl_auto':True,'use_elpa':True},

For calculations with vdW-functionals, use libvdwxc:

xc = {'name':'BEEF-vdW', 'backend':'libvdwxc'},

8. If needed, add fixes.

To do Bayesian error estimation (BEE) see

To use MLMin/NEB apply corrections from

9. Something worth trying:

Atomic Simulation Recipes:


ase-notebook (won’t install at FEND because of glibc 2.17):


gpaw benchmarking:

d4 parameters fitting:

k-point grid choosing:

Useful tips

alt+U to undo
alt+a to start a selection
alt+shift+} to indent the selection

Bayesian Error Estimation (BEE) for RPBE

The Beyesian Error Estimation (BEE) is implemented in GPAW only for PBE, BEEF-vdW, and mBEEF-vdW.

Here is a trick for making the BEE with the RPBE functional. Just edit the lines in ASE and GPAW codes by adding RPBE as an exception.

To find the needed files, run

find ./ -name ""

In ase/dft/ change one line:

class BEEFEnsemble:

            if self.xc in ['BEEF-vdW', 'BEEF', 'PBE', 'RPBE']: # add RPBE
                self.beef_type = 'beefvdw'

In gpaw/xc/ add two lines:

class BEEFEnsemble:
    """BEEF ensemble error estimation."""
    def __init__(self, calc):

        # determine functional and read parameters
        self.xc = self.calc.get_xc_functional()
        if self.xc == 'BEEF-vdW':
            self.bee_type = 1
        elif self.xc == 'RPBE': # catch the RPBE exchange functional
            self.bee_type = 1   # assign BEEF coefficients the RBPE

Below we use BEEF-vdW, RPBE, and PBE dimensionless density (n) with gradient (s) and apply BEEF coefficients (E₀, ΔEᵢ) to evaluate the BEE as the standard deviation for the ensemble total energies with the variable enhancement factor (F(s,θᵢ)).

from ase import Atoms
from ase.dft.bee import BEEFEnsemble
from ase.parallel import parprint
from gpaw import GPAW
import time

for xc in ['BEEF-vdW','RPBE','PBE']:
    start_time = time.time()

    h2 = Atoms('H2',[[0.,0.,0.],[0.,0.,0.741]]) #exp. bond length
    cell = h2.get_cell()

    calc = GPAW(xc=xc,txt='H2_{0}.txt'.format(xc))
    h2.calc = calc
    e_h2 = h2.get_potential_energy()
    ens = BEEFEnsemble(calc)
    de_h2 = ens.get_ensemble_energies()
    del h2, calc, ens

    h = Atoms('H')
    calc = GPAW(xc=xc,txt='H_{0}.txt'.format(xc), hund=True)
    h.calc = calc
    e_h = h.get_potential_energy()
    ens = BEEFEnsemble(calc)
    de_h = ens.get_ensemble_energies()
    del h, calc, ens

    E_bind = 2*e_h - e_h2
    dE_bind = 2*de_h[:] - de_h2[:]
    dE_bind = dE_bind.std()
    parpting('{0} functional'.format(xc))
    parprint('Time: {0} s'.format(round(time.time()-start_time,0)))
    parprint('E_bind: {0} eV'.format(round(E_bind,4)))
    parprint('Error bar {0} eV'.format(round(dE_bind,4)))

TS09 and D4 corrections with ASE

TS09 and D4 are atomic-charge dependent dispersion corrections (see TS09 PRL paper and D4 homepage for the refs). The D4 code is available at github. According to GPAW documentation, TS09 and D4 show for the S26 test set smaller mean deviation than vdW-DF. Herewith, D4 correction does not depend on the actual calculation as it is added to the calculated energy.

Here is how D4 correction can be added with ASE (see Readme) after installing it (for example, as conda install -c conda-forge dftd4 dftd4-python):

from import molecule 
from ase.calculators.mixing import SumCalculator 
from ase.optimize import BFGS
from dftd4.ase import DFTD4 
from gpaw import GPAW 

atoms = molecule('H2O')

gpaw = GPAW(mode='fd',txt='H2O_D4.txt',xc='PBE') 
atoms.calc = SumCalculator([DFTD4(method='PBE'), gpaw])

opt = BFGS(atoms,trajectory='H2O_D4.traj', logfile='H2O_D4.log')

Let me stress that before choosing TS09 or D4 one should consider all pro and contra. TS09 method used Hirshfeld charges while D4 uses the electronegativity equilibration method to obtain charges. The former naturally accounts for the interfacial charge transfer while the latter does not. The TS09 correction requires vdW radii and is implemented for a limited set on functionals (see ASE code), like PBE, RPBE, and BLYP. The D4 correction supports much more functionals (see parameters). Regarding the vdW radii values for TS09 bare in mind that there are four data sources – one in GPAW, two in ASE and one more in ASE.

Here is how TS09 correction can be added with ASE and GPAW:

from import molecule
from ase.calculators.vdwcorrection import vdWTkatchenko09prl
from import vdw_radii
from ase.optimize import BFGS
from gpaw.analyse.hirshfeld import HirshfeldPartitioning
from gpaw.analyse.vdwradii import vdWradii
from gpaw import GPAW

atoms = molecule('H2O')

gpaw = GPAW(mode='fd',txt='H2O_TS.txt',xc='PBE')
atoms.calc = vdWTkatchenko09prl(HirshfeldPartitioning(gpaw), vdWradii(atoms.get_chemical_symbols(), 'PBE'))

opt = BFGS(atoms,trajectory='H2O_TS.traj', logfile='H2O_TS.log')

N.B! Note that the TS09 and D4 energies are no outputted to the H2O.txt. They are written to the log-file.

Installation of LibXC 4.0.0 trunk + GPAW1.3.0 + ASE

Assume that all the requirements are fulfilled:

  • Python 2.7-3.5
  • NumPy 1.6.1 or later (base N-dimensional array package)
  • ASE 3.15.0 or later (atomic simulation environment)
  • a C-compiler
  • LibXC 2.0.1 or later
  • BLAS and LAPACK libraries

Optional, but highly recommended:

  • SciPy 0.7 or later (library for scientific computing, requirered for some features)
  • an MPI library (required for parallel calculations)
  • FFTW (for increased performance)

LibXC compilation:

svn co libxc
cd libxc
autoreconf -i
./configure --enable-shared --prefix=/home/USER/xc
make -j N
make install

The LibXC compilation might not work, and GPAW would complain, so configure as follows:

./configure CFLAGS="-O2 -fPIC" --prefix=/home/USER/xc

After compiling LibXC add these lines to your .bashrc:

export C_INCLUDE_PATH=/home/USER/xc/include
export LIBRARY_PATH=/home/USER/xc/lib
export LD_LIBRARY_PATH=/home/USER/xc/lib

Let’s install ASE using pip, because it is easy.

pip install --upgrade --user ase

Get the GPAW source code and remove in libxc.c in c/xc/ line xc_mgga_x_tb09_set_params(self->functional[0], c);. Them compile GPAW with python install --user. You might want to add the .local/bin to the path.

Use either Python or Python3, and be consistent with that.

The official guideline also recommends adding these lines to your .bashrc:

export PATH=/home/USER/tools:$PATH

Don’t forget to get setups. E.g. execute gpaw install-data DIR. After that run the tests.