General

Introduction

The DFTB engine implements density functional based tight-binding methods, which can be viewed as computationally very efficient approximations to density functional theory (DFT). As such it is a good engine for cheap calculations that still include quantum effects. DFTB is a computational engine that runs through the AMS driver. It can be used directly from the command line, from Python, and through our graphical interface.

What’s new in DFTB?

New in DFTB2024.1

  • Default DOS now divided by DeltaE, just scaling the overall DOS and PDOS, so that the DOS and PDOS have the standard unit (1/(energy*volume)).

New in DFTB2023.1

_images/DFTBSCCfix2022vsfix2023.png

Fig. 1 Comparison of the number of SCC cycles needed. For easy systems there is not much difference, for more difficult systems, however, the fix2023 (green) is an improvement over the fix2022 (red). As there can be some randomness in the number of iterations the calculations are repeated five times (using a different number of cores), the dot is the average number of cycles used, and the vertical lines show the spread in the number of iterations (if any). The maximum number of iterations was set to 500.

New in DFTB2022.1

New in DFTB2021.1

  • The D4 dispersion correction has been added. It can be used with the Slater-Koster based model Hamiltonians and the DFTB.org parameter sets.

New in DFTB2020

  • Calculations with the GFN1-xTB model and many k-points are significantly faster.

  • The default model has been changed from SCC-DFTB to GFN1-xTB, as the latter supports all elements.

  • Various new applications in the AMS driver.

New in DFTB2019.3

New in DFTB2019.1

  • Grimme’s GFN1-xTB has been added as a new model Hamiltonian. It supports molecular as well es periodic calculations for systems including elements up to Radon. Visualization of the results (e.g. molecular orbitals) in AMSview is also supported.

  • Various new applications in the AMS driver.

  • More robust and easier to set up k-space integration.

  • More robust SCC convergence:

    • Adaptive mixing: The charge mixing parameter is automatically decreased if the energy increases during the SCC cycle.

    • The default electronic temperature has been increased to 300K, making SCC convergence more robust for systems with small HOMO-LUMO gaps.

New in DFTB2018

New features

  • Elastic tensor and related properties (e.g. Bulk modulus) (via AMS driver)

  • Linear transit and PES scan (via AMS driver)

  • Geometry optimization under pressure (via AMS driver)

  • …

AMS: a new driver program

Important

In the 2018 release of the Amsterdam Modeling Suite we introduced a new driver program call AMS. We recommend you to first read the General section of the AMS Manual

If you use DFTB exclusively via the Graphical User Interface (GUI), this change should not create any issues. If, on the other hand, you create input files by hand (or you use DFTB via PLAMS), then you should be aware that shell scripts for DFTB2017 and previous versions are not compatible with DFTB2019 and have to be adjusted to the new setup.

The example below shows how a shell script for DFTB2017 is converted to DFTB2019.

DFTB2017 shell script (obsolete):

#!/bin/sh

# This is a shell script for DFTB2017 which will not work for DFTB2019

$AMSBIN/dftb << EOF

Task
   RunType GO
End

System
   Atoms
      H 0.0 0.0 0.0
      H 0.9 0.0 0.0
   End
End

DFTB
   ResourcesDir Dresden
End

Geometry
   iterations 100
End

EOF

DFTB2019 shell script:

#!/bin/sh

# This is a shell script for DFTB2019

# The executable '$AMSBIN/dftb' is no longer present.
# You should use '$AMSBIN/ams' instead.

$AMSBIN/ams << EOF
   # Input options for the AMS driver:

   System
      Atoms
         H 0.0 0.0 0.0
         H 0.9 0.0 0.0
      End
   End

   Task GeometryOptimization

   GeometryOptimization
      MaxIterations 100
   End

   # The input options for DFTB, which are described in this manual,
   # should be specified in the 'Engine DFTB' block:

   Engine DFTB
      ResourcesDir Dresden
   EndEngine
EOF