Metadata-Version: 2.1
Name: pyNNsMD
Version: 1.0.4
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
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
License-File: LICENSE

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# 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)
* [Implementation details](#implementation-details)
* [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="implementation-details"></a>
# Implementation details
TBA

<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

