Materials Design

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1 to 10 of 109 Results

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.
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

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