dftfit 0.4.2

Ab-Initio Molecular Dynamics Potential Development

All issues and contributions should be done on Gitlab. Github is used only as a mirror for visibility

DFTFIT

DFTFIT is a python code that used Ab Initio data from DFT calculations such as VASP and QE to create molecular dynamic potentials. Our package differs from other similar codes in that we leverage LAMMPS.

Presentations:

• HTCMC 2016
• MRS 2017

Algorithm

We use generalized least squares method for finding the optimal parameters for a proposed potential. DFTFIT integrates with existing MD software as a potential calculator. Currently only LAMMPS is supported. This means the user has the freedom to use any of the potentials available in LAMMPS.

Our algorithm follows a highly cited publication that proposes a method for determining a new potential for Silicon using the force matching of DFT calcultions.

Parameters

• n_c: number of system configurations
• N number of atoms in each configuration
• α, β: tensor with 3D dimensions [x, y, z]
• cl: classical results from molecular dynamics potential
• ai: ab initio results from dft simulation
• w_f, w_s, w_e: weights to assign respectively for force, stress, energy
• F, S, E: force, stress, and energy respectively.

Dependencies

• MD Calculator: LAMMPS
• pagmo2
• pymatgen
• Ab Initio data from either VASP or Quantum Espresso

Installation

pip install dftfit

Documentation

The official documentation is hosted on readthedocs.org: https://dftfit.readthedocs.io/en/latest/

Running

DFTFIT is a library that provides methods for optimization. There is a GUI in the works. See the test folder for examples. Currently there are examples for mgo and ceria.

Examples

One example for DFTFIT is included for MgO.

Contributing

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. These should be submitted at the Gitlab repository. Github is only used for visibility.

License

MIT