https://researchdata.se/en/catalogue Search results en Data used in the hands-on notebooks of the CHASAM 2026 school, https://chasam.materialsmodeling.org/. All MD was run with GPUMD using a NEP potential, and each trajectory archive holds the input structure (model.xyz), the NEP model (nep.txt), the GPUMD input (run.in) and the trajectory (dump.xyz). Zenodo record: 10.5281/zenodo.22790961 Files Archive System Contents BaTiO3_models.tar BaTiO3 structures, a pretrained qNEP model and pretrained tensorial NEP models for dipole, polarizability and atomic virial BaTiO3_spectroscopy.tar BaTiO3 GPUMD runs at 100, 240, 360 and 500 K with dipole-derivative (dpdt.out) and polarizability output CsPbI3_NPT_T650to1_size12_nframes2000.tar.gz CsPbI3 NPT cooling run from 650 K to 1 K, 12x12x12 supercell with 8640 atoms, 2000 frames CsPbI3_NVE_T600_size4_nframes100000.tar.gz CsPbI3 NVE run at 600 K, 4x4x4 supercell with 320 atoms, 100000 frames CsPbI3_NVT_T650_size18_nframes500.tar.gz CsPbI3 NVT run at 650 K, 18x18x18 supercell with 29160 atoms, 500 frames day1_basic_training.zip NiAl initial DFT training and validation databases, and one NEP model with five cross-validation splits day2_active_learning.zip NiAl R2SCAN reference databases, validation data and three active-learning generations, each with structure generation, model and tests day2_advanced_nep_hyperparameters.zip NiAl training and validation data, and NEP models scanning n_max, l_max and ZBL, with RMSE and MD-speed summaries day2_advanced_two_stage_training.zip NiAl training and validation data, and single-stage and two-stage NEP models day3_thermodynamic_integration.tar.gz SiO2 Frenkel-Ladd and reversible-scaling GPUMD runs for coesite and stishovite at 8 GPa, holding NPT equilibration at 300 K, five Frenkel-Ladd switching runs per phase and three reversible-scaling ramps per phase from 300 to 1800 K FAPI_md-run-active-learning.tar.gz FAPbI3 NPT heating run from 1 K to 600 K, 2x2x2 supercell with 96 atoms, 2000 frames FAPI_training.tar.gz FAPbI3 DFT databases for two active-learning generations, and NEP models trained on 100 to 900 structures, with and without ZBL mcmd-vcsgc-data.tar.gz FeCr 700 VCSGC Monte Carlo/MD runs of 8000 atoms, at seven temperatures from 300 to 900 K and 100 values of phi from -2.5 to 2.5 with kappa = 200, holding concentration traces (mcmd-*.out) and thermodynamic output (thermo-*.out) Wed, 16 Sep 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-22790961 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-22790961 Chalmers University of Technology Fransson, Erik Berger, Esmée Lindgren, Eric Uddén, Felix Svensson, Lucas Hainer, Tobias Berger, Ethan Ngoipala, Apinya Wiktor, Julia Erhart, Paul Charge-aware machine-learned interatomic potentials and their training data for the three perovskites PbTiO3, CsGeBr3 and CsSnBr3. Files Two files per system, named _: _train.xyz – training set in extended XYZ format, with reference energies, forces and virials. _nep.txt – the NEP potential, usable directly with GPUMD or calorine. Systems PbTiO3 – 345 structures, 5–320 atoms per structure, reference method VASP with r2SCAN. CsGeBr3 – 110 structures, 5–160 atoms per structure, reference method FHI-aims with r2SCAN50. CsSnBr3 – 103 structures, 5–320 atoms per structure, reference method FHI-aims with r2SCAN50. Potentials All three are fourth-generation NEPs with charge support (charge_mode 1, lambda_q 0.1), three species, radial and angular cutoffs of 8 and 4 Å, trained for 300 000 generations. The two halides additionally use a ZBL repulsive term. Wed, 19 Aug 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-22007823 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-22007823 Chalmers University of Technology Fransson, Erik Ngoipala, Apinya Deswal, Priyanka Berger, Ethan Erhart, Paul Wiktor, Julia MA(Ge,Sn)I3 neuroevolution potential and DFT training data Training data and machine-learned interatomic potential for MAGeI3, MASnI3 and their mixed compositions. * `train.xyz` Full training set, 668 structures in extended XYZ format. Reference energies, forces, virials and Born effective charges from FHI-aims.* `nep.txt` The NEP potential, usable directly with GPUMD or calorine.* `control.in` Representative FHI-aims input for a single-point reference calculation. Reference calculations FHI-aims with the hybrid meta-GGA r²SCAN50 (`libxc HYB_MGGA_XC_R2SCAN50`), `intermediate` species defaults, scalar-relativistic `atomic_zora`, `sc_accuracy_rho 1e-6`. The included k_grid is provided as an example and was generated using a k-point density parameter of 5.6, which corresponds to a reciprocal-space sampling of approximately 0.18 Å⁻¹ for the example cell. For other structures, the k_grid should be regenerated from the chosen k-point density. NEP Potential Fourth-generation NEP with charge support (`nep4_zbl_charge1`), six species (C, N, H, Ge, Sn, I), radial and angular cutoffs of 8 and 4 Å, a ZBL repulsive term with `use_typewise_cutoff_zbl 0.7`, and `lambda_q 0.1`.Fitted to the complete `train.xyz` for 300000 generations.Root-mean-square errors against the training data are 4.3 meV/atom for energies, 0.126 eV/Å for forces, 0.020 eV/atom for virials and 0.072 e for Born effective charges. Sun, 09 Aug 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-21863902 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-21863902 Chalmers University of Technology Ngoipala, Apinya Fransson, Erik Erhart, Paul Wiktor, Julia