Data and models supporting "qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations" - Fan, Zheyong; Tang, Benrui; Berger, Esmée; Berger, Ethan; Fransson, Erik; Xu, Ke; Yang, Zihang; Liu, Zhoulin; Song, Zichen; Dong, Haikuan; Chen, Shunda; Li, Lei; et al. (Chalmers University of Technology, 2026)
This record contains models based on the neuroevolution potential (NEP) approach, charge unaware (conventional) NEP models (nep-*.txt) and charge-aware qNEP models (qnep-*.txt). It also contains the respective reference datasets used for training and validation of these models (references-*.txt).

When using any of these models make sure to cite both the original publication for these models as well as, where applicable, the source publications for the reference data (see below).

Sources of reference datasets

Water models

The reference data for the energies, forces, and virials are from

Ke Xu, Yongchao Hao, Ting Liang, Penghua Ying, Jianbin Xu, Jianyang Wu, and Zheyong FanThe Journal of Chemical Physics 158, 204114 (2023)Accurate Prediction of Heat Conductivity of Water by a Neuroevolution Potentialdoi: 10.1063/5.0147039

The reference data for the Born effective charges are from

Zheyong Fan, Benrui Tang, Esmée Berger, Ethan Berger, Erik Fransson, Ke Xu, Zihan Yan, Zhoulin Liu, Zichen Song, Haikuan Dong, Shunda Chen, Ziliang Wang, Lei Li, Yizhou Zhu, Julia Wiktor, and Paul ErhartJournal of Chemical Theory and Computation 22, 4787 (2026)qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulationsdoi: 10.1021/acs.jctc.6c00146

The original structures were generated in

Linfeng Zhang, Han Wang, Roberto Car, and Weinan EPhysical Review Letters 126, 236001 (2021)Phase Diagram of a Deep Potential Water Modeldoi: 10.1103/PhysRevLett.126.236001

Li7La3Zr2O12 garnet models

The reference data are from

Zihan Yan and Yizhou ZhuChemistry of Materials 36, 11551 (2024)Impact of lithium nonstoichiometry on ionic diffusion in tetragonal garnet-type Li7La3Zr2O12doi: 10.1021/acs.chemmater.4c02454

BaTiO3 models

The reference data are from

Zheyong Fan, Benrui Tang, Esmée Berger, Ethan Berger, Erik Fransson, Ke Xu, Zihan Yan, Zhoulin Liu, Zichen Song, Haikuan Dong, Shunda Chen, Ziliang Wang, Lei Li, Yizhou Zhu, Julia Wiktor, and Paul ErhartJournal of Chemical Theory and Computation 22, 4787 (2026)qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulationsdoi: 10.1021/acs.jctc.6c00146

MgOH models

The reference data are from

Z. Liu, J. Sha, G.-L. Song, Z. Wang, and Y. ZhangChemical Engineering Journal 516, 163578 (2025)Understanding magnesium dissolution through machine learning molecular dynamicsdoi: 10.1016/j.cej.2025.163578
Keywords: Neuroevolution potential; Machine-learned interatomic potential; Molecular Dynamics Simulation; Born effective charges; Density functional theory; Physical Sciences; Fysik