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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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|`NepTrainKit`|https://github.com/aboys-cb/NepTrainKit|`NepTrainKit` is a Python package for visualizing and manipulating training datasets for NEP. |
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|`NEP_Active`|https://github.com/psn417/NEP_Active|`NEP_Active` is a python package for building the training set using active learning strategy. It follows the method of Moment Tensor Potential (MTP). |
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