Metadata-Version: 2.1
Name: dftfit
Version: 0.4.3
Summary: Ab-Initio Molecular Dynamics Potential Development
Home-page: https://gitlab.com/costrouc/dftfit
Author: Chris Ostrouchov
Author-email: chris.ostrouchov+dftfit@gmail.com
License: MIT
Download-URL: https://gitlab.com/costrouc/dftfit/repository/archive.zip?ref=v0.4.3
Description: **All issues and contributions should be done on
        [Gitlab](https://gitlab.com/costrouc/dftfit). 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](https://speakerdeck.com/costrouc/dftfit-potential-generation-for-molecular-dynamics-calculations#)
         - [MRS 2017](https://speakerdeck.com/costrouc/dftfit-potential-generation-for-molecular-dynamics-calculations#)
        
        ## 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](http://lammps.sandia.gov/doc/Manual.html) 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](http://dx.doi.org/10.1063/1.1513312) that
        proposes a method for determining a new potential for Silicon using the force matching of DFT calcultions.
        
        ![Optimization Equation](https://gitlab.com/costrouc/dftfit/raw/master/docs/images/equations.png)
        
        ### 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](http://lammps.sandia.gov/)
         - [pagmo2](https://github.com/esa/pagmo2)
         - [pymatgen](https://github.com/materialsproject/pymatgen/)
         - Ab Initio data from either [VASP](https://www.vasp.at/) or [Quantum
           Espresso](http://www.quantum-espresso.org/)
        
        # Installation
        
        ```bash
        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](https://gitlab.com/costrouc/lammps-cython). Github
        is only used for visibility.
        
        # License
        
        [MIT](https://gitlab.com/costrouc/dftfit/blob/master/LICENSE.md)
        
Keywords: materials dft molecular dynamics lammps science hpc
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Description-Content-Type: text/markdown
Provides-Extra: mattoolkit
