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|`pynep`|https://github.com/bigd4/PyNEP|`PyNEP` is a python interface of the machine learning potential NEP used in GPUMD. |
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|`somd`|https://github.com/initqp/somd|`SOMD` is an ab-initio molecular dynamics (AIMD) package designed for the SIESTA DFT code. The SOMD code provides some common functionalities to perform standard Born-Oppenheimer molecular dynamics (BOMD) simulations, and contains a simple wrapper to the Neuroevolution Potential (NEP) package. The SOMD code may be used to automatically build NEPs by the mean of the active-learning methodology. |
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## Authors:
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* Before the first release, GPUMD was developed by Zheyong Fan, with help from Ville Vierimaa (Previously Aalto University) and Mikko Ervasti (Previously Aalto University) and supervision from Ari Harju (Previously Aalto University).
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* Below is the full list of contributors starting from the first release.
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