Metadata-Version: 1.1
Name: dftfit
Version: 0.1.2
Summary: Ab-Initio Molecular Dynamics Potential Development
Home-page: https://gitlab.aves.io/costrouc/dftfit
Author: Chris Ostrouchov
Author-email: chris.ostrouchov+dftfit@gmail.com
License: LGPLv2.1+
Download-URL: https://gitlab.aves.io/costrouc/dftfit/repository/archive.zip?ref=v0.1.2
Description: TODO: much is incorrect needs update
        
        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 about dftfit:
         - [HTCMC 2016](https://speakerdeck.com/costrouc/dftfit-potential-generation-for-molecular-dynamics-calculations#)
        
        Algorithm
        ---------
        
        We use least squares for finding the optimal parameters for a proposed
        potential. Since our DFTFIT uses LAMMPS, the user has the freedom to
        use any of the potentials available in LAMMPS.
        
        Our algorithm follows a
        [highly cited publication](http://dx.doi.org/10.1063/1.1513312) that
        proposes a method for determining a new potential for Silicon.
        
        ![Optimization Equation](docs/img/eqs.png)
        
        Parameters
        ----------
        
         - [$n_c$] number of system configurations
         - [$N$] number of atoms in each configuration
         - [$\alpha, \beta$] 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
        ------------
        
         - [LAMMPS](http://lammps.sandia.gov/)
         - [NLOPT](http://ab-initio.mit.edu/wiki/index.php/NLopt) with python extension or scipy
         - [ASE](https://wiki.fysik.dtu.dk/ase/download.html)
         - DFT data from either [VASP](https://www.vasp.at/) or
           [Quantum Espresso](http://www.quantum-espresso.org/)
        
        Currently DFTFIT depends on the atomic simulation environment but we will be moving to [pymatgen](http://pymatgen.org/) as soon as possible.
        
        
        Install
        -------
        
        ```bash
        python3 setup.py develop --user
        ```
        
        Installing dftfit in this way will allow any changes to the code to be
        immediately applied to the package without the need for a re-install.
        
        Note that DFTFIT will NOT install LAMMPS, VASP, or Quantum Espresso.
        This software must be seperatly installed by the user.
        
        Additionally nlopt is an optional dependency that requires the python
        extension as well. We hope to remove the need for nlopt.
        
        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
        --------
        
        Two examples are included within the dftfit package. Currently it only
        works with the nlopt package. NLOPT requires python 2.7. We hope to
        remove this dependency soon.
        
Keywords: materials dft molecular dynamics lammps science hpc
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
