Institute for Materials Science

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11 to 20 of 114 Results

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
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
12.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 12.9 KB - MD5: 70facc548679742ec5259b11a99f0775
12.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 12.9 KB - MD5: 4ac2a3e6a978e47c9f8d5e1946274195
12.mtp Oct 29, 2024 - Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials Unknown - 12.9 KB - MD5: 264bf4a7c856dd6ce021ca392e7dfe6e

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.

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