Graphical and Python parametrization tools
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Optimize ReaxFF & DFTB parameters

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Optimize ReaxFF, DFTB & MLFF parameters

Easily build your training data and optimize ReaxFF, DFTB and Machine Learning Force Field parameters with the ParAMS module of the Amsterdam Modeling Suite.


ReaxFF Parametrization

Selected features:

  • Import reference data from AMS, VASP, Quantum ESPRESSO, or experiment
  • Fit any number of properties: reaction energies, forces, bond lengths, angles, cell parameters, stress tensors, charges, …
  • Use single points, geometry optimizations, or PES scans during the parametrization
  • Set custom weights for different training set entries
  • Use a validation set to prevent overfitting
  • Choose which parameters to optimize, and set allowed ranges for them
  • Optimize with CMA-ES or Nelder-Mead
  • Intuitive output files for creating correlation plots, energy-volume curves, and more
  • Submit jobs to remote machines using the GUI
  • Results updated on-the-fly in the GUI with many diagrams
LJ Ar fitting

Tutorials and fully worked examples


ParAMS is part of the Advanced workflows and tools module of the Amsterdam Modeling Suite. To fit ReaxFF parameters, you also need a ReaxFF license. To fit DFTB parameters, you also need a DFTB license. For building training data with DFT, an ADF and BAND license will be useful.

ParAMS webinar

SCM’s expert Matti Hellström demonstrates ParAMS and gives some useful tips on how to parametrize your own ReaxFF or DFTB parameters.