`ccu.workflows.calculation`¶

DFT-code agnostic standard calculation workflow utilities.

Example

Relaxation using a Calculator’s internal relaxation algorithms

import logging

from ase.build import bulk
from ase.calculators.vasp.vasp import Vasp
from ccu.adsorption.adsorbates import get_adsorbate
from ccu.workflows.calculation import run_calculation

logging.basicConfig(level=logging.DEBUG)

atoms = bulk("Au") * 3
atoms.center(vacuum=10, axis=2)
surface_atom = max(atoms, key=lambda a: a.position[2])
cooh = get_adsorbate("COOH")
com = cooh.get_center_of_mass()
site = surface_atom.position + [0, 0, 3]
direction = site - com

for atom in cooh:
    atom.position += direction

atoms += cooh

atoms.calc = Vasp(...)
run_calculation(atoms)

Example

Relaxation using an ASE Optimizer

import logging

from ase.build import molecule
from ase.calculators.emt import EMT
from ase.optimize.bfgs import BFGS
from ccu.workflows.calculation import run_calculation

logging.basicConfig(level=logging.DEBUG)

atoms = molecule("CO2")
atoms.calc = EMT()
opt = BFGS(atoms)
run_calculation(atoms, opt=opt, fmax=0.01, steps=10)

Example

Relaxation using a VASP calculation followed by Bader analysis.

import logging

from ase.build import molecule
from ase.calculators.vasp.vasp import Vasp
from ccu.workflows.calculation import run_bader, run_calculation

logging.basicConfig(level=logging.DEBUG)

atoms = molecule("CO2")
atoms.calc = Vasp(
    algo="Normal",
    ediff=1e-8,
    ediffg=-1e-2,
    encut=450,
    gga="PE",
    ivdw=12,
    kpts=(1, 1, 1),
    nelm=50,
    nsw=50,
    prec="Accurate",
)
run_calculation(atoms)
run_bader(atoms.calc.directory)

ccu.workflows.calculation.run_bader(dir_name: str | Path | None = None) → dict[str, Any][source]¶

Run Bader and archive results.

This function is not meant to be run as a workflow but as a post-processing step after calling ccu.workflows.calculation.run_calculation().

Parameters:: dir_name – The directory containing charge density files to be used to run the Henkelman bader software (https://theory.cm.utexas.edu/henkelman/code/bader/).
Returns:: A dictionary mapping to charge density analysis data.

Note

Please cite the following if you use this function:

G. Henkelman, A. Arnaldsson, and H. Jonsson, “A fast and robust algorithm for Bader decomposition of charge density”, Comput. Mater. Sci. 36, 254-360 (2006).

ccu.workflows.calculation.run_calculation(atoms: Atoms, *, opt: Optimizer | None = None, properties: Iterable[str] | None = None, **opt_params: Any) → tuple[Atoms, Optimizer | None][source]¶

Run a calculation.

This workflow can be used to run a single-point calculation (by omitting opt), a calculator-internal relaxation, or a relaxation using an ASE Optimizer.

Parameters:

atoms – An Atoms object with an attached calculator with which to run the calculation.
opt – An ASE Optimizer object that can optionally be used to perform the relaxation. If None, the calculator is used to calculate properties.
properties – An iterable of strings indicating properties to calculate. If opt is None, defaults to ["energy"]. Otherwise, defaults to ["energy", "forces"].
opt_params – Additional keywords passed on to opt.run. Usually, fmax and/or steps.

Returns:

The 2-tuple whose first element is the updated Atoms after the calculation and whose second element is opt.

Raises:

RuntimeError – No calculator set for atoms.

Note

If relaxing with an ASE optimizer, ensure that atoms.calc is configured for a single-point calculation in which forces are calculated.

ccu.workflows.calculation.run_chargemol(dir_name: str | Path | None = None, atomic_densities: str | Path | None = None) → dict[str, Any][source]¶

Run chargemol and archive results.

This function is not meant to be run as a workflow but as a post-processing step after calling ccu.workflows.calculation.run_calculation().

Parameters:

dir_name – The directory containing charge density files to be used to run the Henkelman bader software (https://theory.cm.utexas.edu/henkelman/code/bader/).
atomic_densities – The path to the directory containing the chargemol atomic densities.

Returns:

A dictionary mapping to charge density analysis data.

Note

Please cite the following if you use this function:

Chargemol: (1) T. A. Manz and N. Gabaldon Limas, Chargemol program for performing DDEC analysis, Version 3.5, 2017, ddec.sourceforge.net.

DDEC6 Charges: (1) T. A. Manz and N. Gabaldon Limas, “Introducing DDEC6 atomic population analysis: part 1. Charge partitioning theory and methodology,” RSC Adv., 6 (2016) 47771-47801. (2) N. Gabaldon Limas and T. A. Manz, “Introducing DDEC6 atomic population analysis: part 2. Computed results for a wide range of periodic and nonperiodic materials,” (3) N. Gabaldon Limas and T. A. Manz, “Introducing DDEC6 atomic population analysis: part 4. Efficient parallel computation of net atomic charges, atomic spin moments, bond orders, and more,” RSC Adv., 8 (2018) 2678-2707.

CM5 Charges: (1) A.V. Marenich, S.V. Jerome, C.J. Cramer, D.G. Truhlar, “Charge Model 5: An Extension of Hirshfeld Population Analysis for the Accurate Description of Molecular Interactions in Gaseous and Condensed Phases”, J. Chem. Theory. Comput., 8 (2012) 527-541.

Spin Moments: (1) T. A. Manz and D. S. Sholl, “Methods for Computing Accurate Atomic Spin Moments for Collinear and Noncollinear Magnetism in Periodic and Nonperiodic Materials,” J. Chem. Theory Comput. 7 (2011) 4146-4164.

Bond Orders: (1) “Introducing DDEC6 atomic population analysis: part 3. Comprehensive method to compute bond orders,” RSC Adv., 7 (2017) 45552-45581.

DDEC3 Charges: (1) T. A. Manz and D. S. Sholl, “Improved Atoms-in-Molecule Charge Partitioning Functional for Simultaneously Reproducing the Electrostatic Potential and Chemical States in Periodic and Non-Periodic Materials,” J. Chem. Theory Comput. 8 (2012) 2844-2867. (2) T. A. Manz and D. S. Sholl, “Chemically Meaningful Atomic Charges that Reproduce the Electrostatic Potential in Periodic and Nonperiodic Materials,” J. Chem. Theory Comput. 6 (2010) 2455-2468.

ccu.workflows.calculation¶

`ccu.workflows.calculation`¶