Institute for Materials Science

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11 to 20 of 123 Results

MTP Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Plain Text - 745.0 KB - MD5: d91a6fc1cfcc8b3f529795f431879778 Finally optimized moment tensor potential file
MTP_training_dataset Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Plain Text - 32.7 MB - MD5: 298a742a3798cf8d7321f86c95a651f4 Training dataset for 24g MTP (2591 cfgs in total)
POSCARs.zip Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten ZIP Archive - 26.4 KB - MD5: 6e6e36dec5a8361508601b8a3111ae5d All DFT and thermodynamic integration POSCAR files
Source_Data.xlsx Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten MS Excel Spreadsheet - 100.3 MB - MD5: f0211e7ab7a7df1bbf6109a37cc63076 All data presented in graphs within the Figures and Supplementary Figures.
Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Oct 29, 2024 - Materials Design Ou, Yongliang; Ikeda, Yuji; Scholz, Lena; Divinski, Sergiy; Fritzen, Felix; Grabowski, Blazej, 2024, "Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials", https://doi.org/10.18419/DARUS-4510, DaRUS, V1 The data in this repository support the findings presented in the article "Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials" by Ou et al. The repository contains the training sets, the fitted machine-learning interatomic potentials (MTPs), and the relaxed bulk and gr...
10.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 11.1 KB - MD5: 5bf3b371ba4aaea15e7602a47e9bdc86
10.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 11.1 KB - MD5: a4a4cc9209c5edad0af3c484f4db8545
10.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 11.1 KB - MD5: 649e2446f8179fbb7745a7eaff64fff4
10.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 11.1 KB - MD5: 0976a5d6d74cd04505b383e3c88f46b2
10.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 11.1 KB - MD5: 9120e4b35026d9e45db02703b1214d37

MTP

Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

Plain Text - 745.0 KB -

Finally optimized moment tensor potential file

MTP_training_dataset

Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

Plain Text - 32.7 MB -

Training dataset for 24g MTP (2591 cfgs in total)

POSCARs.zip

Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

ZIP Archive - 26.4 KB -

All DFT and thermodynamic integration POSCAR files

Source_Data.xlsx

Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

MS Excel Spreadsheet - 100.3 MB -

All data presented in graphs within the Figures and Supplementary Figures.

Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

Oct 29, 2024 - Materials Design

Ou, Yongliang; Ikeda, Yuji; Scholz, Lena; Divinski, Sergiy; Fritzen, Felix; Grabowski, Blazej, 2024, "Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials", https://doi.org/10.18419/DARUS-4510, DaRUS, V1

The data in this repository support the findings presented in the article "Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials" by Ou et al. The repository contains the training sets, the fitted machine-learning interatomic potentials (MTPs), and the relaxed bulk and gr...

10.mtp

Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

Unknown - 11.1 KB -

10.mtp

Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

Unknown - 11.1 KB -

10.mtp

Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

Unknown - 11.1 KB -

10.mtp

Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

Unknown - 11.1 KB -