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fix readme #700

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fix readme #700

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2 changes: 1 addition & 1 deletion potentials/nep/readme.md
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| NEP model file | Paper for the training data | Paper for the NEP model | Comments |
| --------- | -------------------------------------- | -------------------------------------- | ------------------- |
| Si_2022_NEP2.txt | Albert P. Bartók et al., [Machine Learning a General-Purpose Interatomic Potential for Silicon](https://doi.org/10.1103/PhysRevX.8.041048), Phys. Rev. X **8**, 041048 (2018). | Zheyong Fan et al., [Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport](https://doi.org/10.1103/PhysRevB.104.104309), Phys. Rev. B. **104**, 104309 (2021). | General-purpose potential for silicon |
| Si_2022_NEP3_3body.txt; Si_2022_NEP3_4body.txt; Si_2022_NEP3_5body.txt | Albert P. Bartók et al., [Machine Learning a General-Purpose Interatomic Potential for Silicon](https://doi.org/10.1103/PhysRevX.8.041048), Phys. Rev. X **8**, 041048 (2018). | Zheyong Fan et al., [GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations](https://aip.scitation.org/doi/10.1063/5.0106617), The Journal of Chemical Physics **157**, 114801 (2022). | General-purpose potential for silicon |
| C_2022_NEP3.txt | Volker L. Deringer et al., [Machine learning based interatomic potential for amorphous carbon](https://doi.org/10.1103/PhysRevB.95.094203), Phys. Rev. B **95**, 094203 (2017). | Zheyong Fan et al., [GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations](https://aip.scitation.org/doi/10.1063/5.0106617), The Journal of Chemical Physics **157**, 114801 (2022). | Mainly for amorphous carbon |
| C_2024_NEP4.txt | Patrick Rowe et al., [An accurate and transferable machine learning potential for carbon](https://doi.org/10.1063/5.0005084), J. Chem. Phys. **153**, 034702 (2020); | Zheyong Fan et al., [Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials](https://doi.org/10.1088/1361-648X/ad31c2). Journal of Physics: Condensed Matter **36**, 245901 (2024) | General-purpose potential for carbon |
| Song-2024-UNEP-v1-AgAlAuCrCuMgMoNiPbPdPtTaTiVWZr.txt | Keke Song et al., [General-purpose machine-learned potential for 16 elemental metals and their alloys](https://doi.org/10.48550/arXiv.2311.04732), arXiv:2311.04732 [cond-mat.mtrl-sci] | Keke Song et al., [General-purpose machine-learned potential for 16 elemental metals and their alloys](https://doi.org/10.48550/arXiv.2311.04732), arXiv:2311.04732 [cond-mat.mtrl-sci] | General-purpose potential for 16 metals and their arbitrary alloys |
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