https://researchdata.se/en/catalogue Search results en This record contains machine-learned interatomic potential (MLIP) and cluster expansion (CE) models for the AuCuPd:CO,H system along with reference data from density functional theory (DFT) calculations. Technical details are provided in Pernilla Ekborg-Tanner and Paul ErhartCompeting adsorption of H and CO on Pd-alloy surfaces: Mechanistic insight into the mitigating effect of Cu on CO poisoningdoi: https://doi.org/10.48550/arXiv.2603.00776 MLIP models Models based on the neuroevolution potential (NEP) architecture are provided in the nep-*.txt files. The "full" model has been trained against all available data whereas the "split" models have been trained against a random selection of 90% structures from the available structures. Models based on the MACE architecture are provided in the MACE-*.model files. CE models Models are provided for (111), (110), and (100) surfaces in the *.ce files along with reference structures in the *.xyz files Reference data Reference data from DFT calculations is provided in the reference-structures.db file, which is a sqlite database generated using the ase package. Sat, 28 Feb 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-17670909 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-17670909 Chalmers University of Technology Ekborg-Tanner, Pernilla Erhart, Paul 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 contains neuroevolution potential (NEP) and tensor neuroevolution potential (TNEP) models (nep*.txt) for molecular water species, liquid water as well as barium zirconate, along with training data (*.zip). The models were constructed using GPUMD 3.9 (https://gpumd.org/). Mon, 04 Dec 2023 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-10257363 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-10257363 Chalmers University of Technology Xu, Nan Rosander, Petter Schäfer, Christian Lindgren, Eric Österbacka, Nicklas Fang, Mandi Chen, Wei He, Yi Fan, Zheyong Erhart, Paul