https://researchdata.se/en/catalogue Search results en 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 ErhartqNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulationsdoi: https://doi.org/10.48550/arXiv.2601.19034 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 ErhartqNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulationsdoi: https://doi.org/10.48550/arXiv.2601.19034 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 Thu, 22 Jan 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-18335947 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-18335947 Chalmers University of Technology Fan, Zheyong Tang, Benrui Berger, Esmée Berger, Ethan Fransson, Erik Xu, Ke Yang, Zihang Liu, Zhoulin Song, Zichen Dong, Haikuan Chen, Shunda Li, Lei Wang, Ziliang Zhu, Yizhou Wiktor, Julia Erhart, Paul This record accompanies the publication "Predicting neutron experiments from first principles: A workflow powered by machine learning". It comprises the machine-learned interatomic potentials (MLIPs) constructed and employed in that work with their respective training data as well as the experimental inelastic neutron scattering data for crystalline benzene presented in the publication. Hydrogenated Sc-doped BaTiO3 nep-BaScTiOH.txt – MLIP based on the neuroevolution potential (NEP) form nep-BaScTiOH.zip – model ensemble with the underlying training and validation data BaScTiOH-R2SCAN.db – database with reference data, in sql-lite format, readable using the ase package Benzene nep-benzene.txt – MLIP based on the neuroevolution potential (NEP) form nep-benzene.zip – model ensemble with the underlying training and validation data benzene-CX.db – database with reference data, in sql-lite format, readable using the ase package reduced-benzene-tosca.zip – experimental inelastic neutron scattering data Fri, 04 Jul 2025 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-15809229 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-15809229 Chalmers University of Technology Lindgren, Eric Jackson, Adam J. Fransson, Erik Berger, Esmée Rudic, Svemir Skoro, Goran Turanyi, Rastislav Mukhopadhyay, Sanghamitra Erhart, Paul This record contains data and code that accompany the paper "Optical line shapes of color centers in solids from classical autocorrelation functions". Specifically it includea databases in ase sqlite format (*.db) with reference data from density functional theory calculations. These data were used in the construction of the machine-learned potential model using the neuroevolution potential (NEP) methodology. The model (nep.txt) is included in a format suitable for the GPUMD package (https://gpumd.org). Note that the atom type information in the databases already includes the labeling of the defect environment that is expected by the NEP model, according to the following rules Si(gs) →  P C(gs) → N Si(ex) → S C(ex) → O The "bulk" species (Si, C) remain unchanged. Fri, 09 Aug 2024 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-13284739 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-13284739 Chalmers University of Technology Linderälv, Christopher Österbacka, Nicklas Wiktor, Julia Erhart, Paul This data set contains data and code related to the publication "Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy". The updated record contains force constant potential (FCP) files that are readable by hiphive 1.X The old versions require a version Sat, 26 Jun 2021 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-7915677 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-7915677 Chalmers University of Technology Brorsson, Joakim Hashemi, Arsalan Fan, Zheyong Fransson, Erik Eriksson, Fredrik Ala-Nissila, Tapio Krasheninnikov, Arkady V. Komsa, Hannu-Pekka Erhart, Paul