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DFTK: The Density-functional toolkit

Herbst, Michael F.; Levitt, Antoine; Cancès, Eric

DFTK v0.6.0

Diff since v0.5.15

Note that this release increases the minor version and contains a number of breaking changes:

  • The self_consistent_field and potential_mixing SCF functions now check convergence in the density by default. This means that self_consistent_field(basis; tol=1e-6) now converges until the change in density between subsequent steps is below 1e-6. Before it used to converge until the change in energy was less than tol. To recover the old behaviour use self_consistent_field(basis; is_converged=DFTK.ScfConvergenceEnergy(tol)). (#800)

  • The load_lattice, load_atoms and load_positions functions are now deprecated and will be removed in DFTK 0.6.1. Users are advised to use AtomsIO for the purpose of loading structures from external files. See and for examples.

  • Instead of using NLSolve's Anderson acceleration we have switched to a built-in implementation, which we found more stable in practice. (#791) The NLSolve-been approach has been removed completely.

  • The deprecated G_to_r and r_to_G functions for FFTs have now been removed in favour of ifft, fft and similar. (#804)

  • The deprecated n_bands and n_ep_extra arguments from self_consistent_field have now been removed. (#804)

Closed issues:

  • Struct for atoms (#118)
  • GPU discussion (#350)
  • SCF default tolerance should be on the density (#780)
  • Run GPU CI only after activation (#803)

Merged pull requests:

  • Bugfix for computations of δψ and supercells (#775) (@epolack)
  • Switch default scf solver to builtin Anderson (#791) (@epolack)
  • Add developper documentation for GPU computations (#793) (@GVigne)
  • Correct PW parameterization comment (#795) (@epolack)
  • Add GPU CI via gitlab (#796) (@mfherbst)
  • Switch to density tolerance by default (#800) (@mfherbst)
  • Make cigpu badge a link to ci pipelines in gitlab (#801) (@carstenbauer)
  • Rely on AtomsIO for structure parsing (#802) (@mfherbst)
  • Remove deprecated features (#804) (@mfherbst)
Cite this paper whenever you use DFTK.
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