Ab-Initio Molecular Dynamics Potential Development
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.
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.
- 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.
pip install dftfit
The official documentation is hosted on readthedocs.org: https://dftfit.readthedocs.org/
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.
One example for DFTFIT is included for MgO.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|dftfit-0.3.2-py3-none-any.whl (39.2 kB) Copy SHA256 hash SHA256||Wheel||py3||Mar 30, 2018|
|dftfit-0.3.2.tar.gz (26.6 kB) Copy SHA256 hash SHA256||Source||None||Mar 30, 2018|