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1 to 10 of 13 Results

Data for: Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs Dec 17, 2025 - Materials Design Forslund, Axel; Jung, Jong Hyun; Ikeda, Yuji; Grabowski, Blazej, 2025, "Data for: Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs", https://doi.org/10.18419/DARUS-4999, DaRUS, V1 Data for the Publication Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs This data set contains: Data for/from the direct upsampling for the rung 1–3 functionals: The effective harmonic potentials The final moment-tensor potentials (for each phase and functional) The train...
Replication Data for: Lattice distortions and non-sluggish diffusion in BCC refractory high entropy alloys Oct 30, 2025 - Materials Design Zhang, Xi; Xiang Xu; Körmann, Fritz; Divinski, Sergiy; Grabowski, Blazej, 2025, "Replication Data for: Lattice distortions and non-sluggish diffusion in BCC refractory high entropy alloys", https://doi.org/10.18419/DARUS-5507, DaRUS, V1 The original experiment and simulation data for reproducing the key results in the publication "Lattice distortions and non-sluggish diffusion in BCC refractory high entropy alloys" (Acta Materialia 297 (2025) 121283)
Data for: Machine learning potentials for hydrogen absorption in TiCr2 Laves phases Jul 22, 2025 - Materials Design Kumar, Pranav; Körmann, Fritz; Grabowski, Blazej; Ikeda, Yuji, 2025, "Data for: Machine learning potentials for hydrogen absorption in TiCr2 Laves phases", https://doi.org/10.18419/DARUS-5169, DaRUS, V1 This dataset supports the development and validation of machine learning interatomic potentials (MLIPs) for modeling hydrogen absorption in C14 (hexagonal) and C15 (cubic) TiCr₂-based Laves phases. It includes density functional theory (DFT) calculations, fitted moment tensor potentials (Level 16), and basin-hopping Monte Carlo (BHMC)-sampled struc...
Data for: Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study Jun 17, 2025 - Materials Design Ikeda, Yuji; Körmann, Fritz, 2025, "Data for: Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study", https://doi.org/10.18419/DARUS-5117, DaRUS, V1 Data for the Publication Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study The dataset contains the DFT data (VASP OUTCARs) that can reproduce the results. The following systems are included: N2 molecule CrMnFeCoNi without interstitial N CrMnFeCoNi with N at the octahedral and the tetrahedral interst...
Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Jan 22, 2025 - Materials Design Zhang, Xi; Divinski, Sergiy; Grabowski, Blazej, 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; Zhang, Xi; Ruban, Andrei; Schmauder, Siegfried; Grabowski, Blazej, 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: Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs

Dec 17, 2025 - Materials Design

Forslund, Axel; Jung, Jong Hyun; Ikeda, Yuji; Grabowski, Blazej, 2025, "Data for: Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs", https://doi.org/10.18419/DARUS-4999, DaRUS, V1

Data for the Publication Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs This data set contains: Data for/from the direct upsampling for the rung 1–3 functionals: The effective harmonic potentials The final moment-tensor potentials (for each phase and functional) The train...

Replication Data for: Lattice distortions and non-sluggish diffusion in BCC refractory high entropy alloys

Oct 30, 2025 - Materials Design

Zhang, Xi; Xiang Xu; Körmann, Fritz; Divinski, Sergiy; Grabowski, Blazej, 2025, "Replication Data for: Lattice distortions and non-sluggish diffusion in BCC refractory high entropy alloys", https://doi.org/10.18419/DARUS-5507, DaRUS, V1

The original experiment and simulation data for reproducing the key results in the publication "Lattice distortions and non-sluggish diffusion in BCC refractory high entropy alloys" (Acta Materialia 297 (2025) 121283)

Data for: Machine learning potentials for hydrogen absorption in TiCr2 Laves phases

Jul 22, 2025 - Materials Design

Kumar, Pranav; Körmann, Fritz; Grabowski, Blazej; Ikeda, Yuji, 2025, "Data for: Machine learning potentials for hydrogen absorption in TiCr2 Laves phases", https://doi.org/10.18419/DARUS-5169, DaRUS, V1

This dataset supports the development and validation of machine learning interatomic potentials (MLIPs) for modeling hydrogen absorption in C14 (hexagonal) and C15 (cubic) TiCr₂-based Laves phases. It includes density functional theory (DFT) calculations, fitted moment tensor potentials (Level 16), and basin-hopping Monte Carlo (BHMC)-sampled struc...

Data for: Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study

Jun 17, 2025 - Materials Design

Ikeda, Yuji; Körmann, Fritz, 2025, "Data for: Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study", https://doi.org/10.18419/DARUS-5117, DaRUS, V1

Data for the Publication Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study The dataset contains the DFT data (VASP OUTCARs) that can reproduce the results. The following systems are included: N2 molecule CrMnFeCoNi without interstitial N CrMnFeCoNi with N at the octahedral and the tetrahedral interst...

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

Jan 22, 2025 - Materials Design

Zhang, Xi; Divinski, Sergiy; Grabowski, Blazej, 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; Zhang, Xi; Ruban, Andrei; Schmauder, Siegfried; Grabowski, Blazej, 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...

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