Metadata-Version: 2.1
Name: pyNNsMD
Version: 1.0.2
Summary: Neural Network for learning potential energy surface for molecular dynamics.
Home-page: UNKNOWN
Author: Patrick Reiser
Author-email: patrick.reiser@kit.edu
License: UNKNOWN
Description: [![Documentation Status](https://readthedocs.org/projects/pynnsmd/badge/?version=latest)](https://pynnsmd.readthedocs.io/en/latest/?badge=latest)
        
        # NNsForMD
        
        Neural network class for molecular dynamics to predict potential energy, gradients and non-adiabatic couplings (NACs).
        
        # Table of Contents
        * [General](#general)
        * [Installation](#installation)
        * [Documentation](#documentation)
        * [Examples](#examples)
        * [Usage](#usage)
        * [Citing](#citing)
        * [References](#references)
        
        <a name="general"></a>
        # General
        This repo is currently under construction. The original version used as the PyRAI2MD interface is v1.0.0.
        
        
        
        <a name="installation"></a>
        # Installation
        
        Clone repository https://github.com/aimat-lab/NNsForMD and install for example with editable mode:
        
        ```bash
        pip install -e ./pyNNsMD
        ```
        or latest release via Python Package Index.
        
        ```bash
        pip install pyNNsMD
        ```
        
        <a name="documentation"></a>
        # Documentation
        
        Auto-documentation generated at https://pynnsmd.readthedocs.io/en/latest/index.html
        
        <a name="examples"></a>
        # Examples
        
        A set of examples can be found in [examples](examples), that demonstrate usage and typical tasks for projects.
        
        <a name="usage"></a>
        # Usage
        TBA
        
        <a name="citing"></a>
        # Citing
        
        If you want to cite this repository or the PyRAI2MD code, please refer to our publication at:
        ```
        @Article{JingbaiLi2021,
            author ="Li, Jingbai and Reiser, Patrick and Boswell, Benjamin R. and Eberhard, AndrÃ© and Burns, Noah Z. and Friederich, Pascal and Lopez, Steven A.",
            title  ="Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations",
            journal  ="Chem. Sci.",
            year  ="2021",
            pages  ="-",
            publisher  ="The Royal Society of Chemistry",
            doi  ="10.1039/D0SC05610C",
            url  ="http://dx.doi.org/10.1039/D0SC05610C"
        }
        ```
        
        <a name="references"></a>
        # References
        
        * https://onlinelibrary.wiley.com/doi/full/10.1002/qua.24890
        * https://pubs.acs.org/doi/abs/10.1021/acs.chemrev.0c00749
Keywords: materials,science,machine,learning,deep,dynamics,molecular,potential
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: tf
Provides-Extra: tf_gpu
