.. 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
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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
[doi:10.1016/j.cpc.2016.05.010]
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
[DOI:10.1063/1.4960708]
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
[DOI:10.1039/C7CP00375G]
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
DOI:10.1063/5.0079314