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1 to 10 of 117 Results

Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Jan 22, 2025 Zhang, Xi, 2024, "Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten", https://doi.org/10.18419/DARUS-4564, DaRUS, V2 The dataset contains key files to reproduce the results presented in the article " Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten": DFT input files: INCAR, KPOINTS. All POSCAR files for DFT and thermodynamic integration Moment tensor potential (MTP) file Training dataset for MTP All Hessian Matri...
DFT_input.zip Jan 22, 2025 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten ZIP Archive - 2.4 KB - MD5: d35f986a660e6a0e85b9a0835f764de8 DFT input files for various calculations: INCARs and KPOINTS
Hesse_Matrices.zip Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten ZIP Archive - 13.1 MB - MD5: 4f1b005bb20cbed2bc070f9717ef574b All Hessian Matrix files for thermodynamic integration
Imag_Modes.zip Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten ZIP Archive - 32.5 KB - MD5: a56b391cbf1092bd61d6f24efafcd018 All imaginary mode files for transition state
Lattice_expansion.dat Nov 11, 2024 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Fixed Field Text Data - 102 B - MD5: 8e7ff7329a52855e2dc501f30332585f Utilized lattice expansion
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 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...

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

Jan 22, 2025

Zhang, Xi, 2024, "Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten", https://doi.org/10.18419/DARUS-4564, DaRUS, V2

The dataset contains key files to reproduce the results presented in the article " Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten": DFT input files: INCAR, KPOINTS. All POSCAR files for DFT and thermodynamic integration Moment tensor potential (MTP) file Training dataset for MTP All Hessian Matri...

DFT_input.zip

Jan 22, 2025 - Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

ZIP Archive - 2.4 KB -

DFT input files for various calculations: INCARs and KPOINTS

Hesse_Matrices.zip

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

ZIP Archive - 13.1 MB -

All Hessian Matrix files for thermodynamic integration

Imag_Modes.zip

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

ZIP Archive - 32.5 KB -

All imaginary mode files for transition state

Lattice_expansion.dat

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

Fixed Field Text Data - 102 B -

Utilized lattice expansion

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

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...

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