.. Amp documentation master file, created by
   sphinx-quickstart on Thu Jul 30 17:27:50 2015.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Credits
=======

People
------

This project is developed primarily by **Andrew Peterson** and **Alireza Khorshidi** in the Brown University School of Engineering. Specific credits:

* Andrew Peterson: lead, PI, many modules
* Alireza Khorshidi: many modules, Zernike descriptor
* Zack Ulissi: tensorflow version of neural network
* Muammar El Khatib: general contributions


We are also indebted to Nongnuch Artrith (MIT) and Pedro Felzenszwalb (Brown) for inspiration and technical discussion.

Citations
---------

We would appreciate if you cite the below publication for any use of Amp or its methods:

    Khorshidi & Peterson, "Amp: A modular approach to machine learning in atomistic simulations", *Computer Physics Communications* 207:310-324, 2016. |amp_paper|


.. |amp_paper| raw:: html

   <a href="http://dx.doi.org/10.1016/j.cpc.2016.05.010" target="_blank">[doi:10.1016/j.cpc.2016.05.010] </a>


If you use Amp for saddle-point searches or nudged elastic bands, please also cite:

    Peterson, "Acceleration of saddle-point searches with machine learning", *Journal of Chemical Physics*, 145:074106, 2016. |mlneb_paper|


.. |mlneb_paper| raw:: html

   <a href="http://dx.doi.org/10.1063/1.4960708" target="_blank">[DOI:10.1063/1.4960708] </a>

If you use Amp for uncertainty or with the bootstrap module, please also cite:

    Peterson, Christensen, Khorshidi, "Addressing uncertainty in atomistic machine learning", *Physical Chemistry Chemical Physics*, 19:10978, 2017. |bootstrap_paper|


.. |bootstrap_paper| raw:: html

   <a href="http://dx.doi.org/10.1039/C7CP00375G" target="_blank">[DOI:10.1039/C7CP00375G] </a>

If you use the initialization, feature and image selection, or nearsighted force training modules, we would appreaciate it if you can also cite:

    Zeng, Chen and Peterson, "A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures ". *JCP* 156, 064104 (2022). |nft_paper|

.. |nft_paper| raw:: html

    <a href="https://aip.scitation.org/doi/10.1063/5.0079314" target="_blank">DOI:10.1063/5.0079314</a>