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1 to 9 of 9 Results

Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Jan 22, 2025 - Materials Design 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...
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...
Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom Mar 8, 2024 - Materials Design Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej; Shapeev, Alexander, 2024, "Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom", https://doi.org/10.18419/DARUS-3891, DaRUS, V1 Data for "Srinivasan, P., Demuriya, D., Grabowski, B. et al. Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Comput Mater 10, 41 (2024). doi:10.1038/s41524-024-01222-9 The dataset contains three folders: Data for the four figures in the manuscript. This also includes the thermodynamic properties w...
Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition Dec 14, 2023 - Materials Design Jung, Jong Hyun; Forslund, Axel; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition", https://doi.org/10.18419/DARUS-3582, DaRUS, V1, UNF:6:PcXLVWUQ0T4geRQy0F0sgg== [fileUNF] Data for the publication, Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, Phys. Rev. B 108, 184107 (2023). This data set contains 1) - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs (for seperate hcp and bcc phases and combined...
Replication Data for: Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al Jun 30, 2023 - PN 3-10 Xu, Xiang, 2023, "Replication Data for: Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al", https://doi.org/10.18419/DARUS-3579, DaRUS, V1 Data for the publication " Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al", Acta Materialia, 255, doi: 10.1016/j.actamat.2023.118986. This data set contains the training sets (VASP files), the utilized moment-tensor-potentials (MTP) and the final thermodynamic database (properties) for the three...
Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems May 26, 2023 - Materials Design Gubaev, Konstantin; Zaverkin, Viktor; Srinivasan, Prashanth; Duff, Andrew; Kästner, Johannes; Grabowski, Blazej, 2023, "Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems", https://doi.org/10.18419/DARUS-3516, DaRUS, V1 Data for the publication "Performance of two complementary machine-learned potentials in modelling chemically complex systems", npj. Comp. Mat. This data set contains the datasets of structures in cfg and npz formats INCAR file which was used for VASP calculations python script for reading npz format These are essentially the 2-, 3-, and 4-componen...
Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals May 12, 2023 - Materials Design Forslund, Axel; Jung, Jong Hyun; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals", https://doi.org/10.18419/DARUS-3339, DaRUS, V1 Data for the publication Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). This data set contains - the training sets (VASP files), - the low moment-tensor potentials (MTPs) and high-MTPs, - t...
Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials Jan 11, 2023 - Materials Design Jung, Jong Hyun; Srinivasan, Prashanth; Forslund, Axel; Grabowski, Blazej, 2023, "Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", https://doi.org/10.18419/DARUS-3239, DaRUS, V1 Data for the publication High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, npj Comput. Mater., DOI: 10.1038/s41524-022-00956-8 (2023) This data set contains - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs, - the effective quasiharmo...
Materials Design(Universität Stuttgart) Oct 24, 2022

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

Jan 22, 2025 - Materials Design

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...

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...

Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom

Mar 8, 2024 - Materials Design

Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej; Shapeev, Alexander, 2024, "Data for: Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom", https://doi.org/10.18419/DARUS-3891, DaRUS, V1

Data for "Srinivasan, P., Demuriya, D., Grabowski, B. et al. Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Comput Mater 10, 41 (2024). doi:10.1038/s41524-024-01222-9 The dataset contains three folders: Data for the four figures in the manuscript. This also includes the thermodynamic properties w...

Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition

Dec 14, 2023 - Materials Design

Jung, Jong Hyun; Forslund, Axel; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition", https://doi.org/10.18419/DARUS-3582, DaRUS, V1, UNF:6:PcXLVWUQ0T4geRQy0F0sgg== [fileUNF]

Data for the publication, Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, Phys. Rev. B 108, 184107 (2023). This data set contains 1) - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs (for seperate hcp and bcc phases and combined...

Replication Data for: Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al

Jun 30, 2023 - PN 3-10

Xu, Xiang, 2023, "Replication Data for: Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al", https://doi.org/10.18419/DARUS-3579, DaRUS, V1

Data for the publication " Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al", Acta Materialia, 255, doi: 10.1016/j.actamat.2023.118986. This data set contains the training sets (VASP files), the utilized moment-tensor-potentials (MTP) and the final thermodynamic database (properties) for the three...

Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems

May 26, 2023 - Materials Design

Gubaev, Konstantin; Zaverkin, Viktor; Srinivasan, Prashanth; Duff, Andrew; Kästner, Johannes; Grabowski, Blazej, 2023, "Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems", https://doi.org/10.18419/DARUS-3516, DaRUS, V1

Data for the publication "Performance of two complementary machine-learned potentials in modelling chemically complex systems", npj. Comp. Mat. This data set contains the datasets of structures in cfg and npz formats INCAR file which was used for VASP calculations python script for reading npz format These are essentially the 2-, 3-, and 4-componen...

Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals

May 12, 2023 - Materials Design

Forslund, Axel; Jung, Jong Hyun; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals", https://doi.org/10.18419/DARUS-3339, DaRUS, V1

Data for the publication Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). This data set contains - the training sets (VASP files), - the low moment-tensor potentials (MTPs) and high-MTPs, - t...

Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials

Jan 11, 2023 - Materials Design

Jung, Jong Hyun; Srinivasan, Prashanth; Forslund, Axel; Grabowski, Blazej, 2023, "Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", https://doi.org/10.18419/DARUS-3239, DaRUS, V1

Data for the publication High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, npj Comput. Mater., DOI: 10.1038/s41524-022-00956-8 (2023) This data set contains - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs, - the effective quasiharmo...

Materials Design(Universität Stuttgart)

Oct 24, 2022

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