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# Collections

`s22`, `dcdft`, `g2`

class ase.collections.collection.Collection(name)

  Collection of atomic configurations and associated data.

  Example of use:

      >>> from ase.collections import s22
      >>> len(s22)
      22
      >>> s22.names[:3]
      ['Ammonia_dimer', 'Water_dimer', 'Formic_acid_dimer']
      >>> dimer = s22['Water_dimer']
      >>> dimer.get_chemical_symbols()
      ['O', 'H', 'H', 'O', 'H', 'H']
      >>> s22.data['Ammonia_dimer']
      {'cc_energy': -0.1375}
      >>> sum(len(atoms) for atoms in s22)
      414

  Create a collection lazily.

  Will read data from json file when needed.

  A collection can be iterated over to get the Atoms objects and indexed with names to get individual members.

  Attributes:

  name: str

    Name of collection.

  data: dict

    Data dictionary.

  filename: str

    Location of json file.

  names: list

    Names of configurations in the collection.

## S22 database of weakly interacting dimers and complexes

ase.collections.s22

S22 geometry data are from:

> 16. Jurecka, J. Sponer, J. Cerny, P. Hobza; Phys Chem Chem Phys 2006, 8 (17), 1985-1993.

See http://www.begdb.com/index.php?action=106a6c241b8797f52e1e77317b96a201 for the original files. All geometries are optimized at either the CCSD(T) or MP2 level except for the methyl amide dimers where only the hydrogen position is optimized at the DFT level.

The S22 interaction energies are all calculated using both CCSD(T)/CBS counter poised corrected (CP) and MP2 /CBS CP. The original S22 interaction energies are listed in the above references. The S22 energies used here are from Takatani, T. et al., J. Chem. Phys., 132, 144104 (2010) where a large and more complete basis set has been used for all database members.

## DeltaCodesDFT

ase.collections.dcdft

Structures and data from:

> molmod/DeltaCodesDFT

See also

- [Lejaeghere2014], [Lejaeghere2016].

- https://molmod.ugent.be/deltacodesdft

[Lejaeghere2014]

K. Lejaeghere, V. Van Speybroeck, G. Van Oost, and S. Cottenier: “Error estimates for solid-state density-functional theory predictions: an overview by means of the ground-state elemental crystals”, Crit. Rev. Solid State (2014). doi: 10.1080/10408436.2013.772503

[Lejaeghere2016]

Kurt Lejaeghere et al.: “Reproducibility in density functional theory calculations of solids”, Science 351 (6280), aad3000 (2016). doi: 10.1126/science.aad3000

This collection has WIEN2k and experimental data for:

- volume per atom

- bulk-modulus (in GPa)

- pressure derivative of bulk-modulus

    >>> from ase.collections import dcdft
    >>> dct = dcdft.data['Cu']
    >>> for key, val in sorted(dct.items()):
    ...     print('{:15}: {:.3f}'.format(key, val))
    exp_B          : 144.279
    exp_Bp         : 4.880
    exp_volume     : 11.647
    wien2k_B       : 141.335
    wien2k_Bp      : 4.860
    wien2k_volume  : 11.951

See also

Calculating Delta-values.

## G2 neutral test set of molecules

ase.collections.g2

Molecules from [Curtiss1997].

[Curtiss1997]

Larry A. Curtiss, Krishnan Raghavachari, Paul C. Redfern, John A. Pople: “Assessment of Gaussian-2 and density functional theories for the computation of enthalpies of formation”, J. Chem. Phys. 106, 1063 (1997). doi: 10.1063/1.473182