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1 to 10 of 15 Results

Data for: Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions Feb 24, 2026 Ikeda, Yuji; Forslund, Axel; Kumar, Pranav; Ou, Yongliang; Jung, Jong Hyun; Koehn, Andreas; Grabowski, Blazej, 2026, "Data for: Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions", https://doi.org/10.18419/DARUS-5272, DaRUS, V1 Data for reproducing the results in the manuscript "Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions". Find further details in README.md.
Replication Data for: Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions Feb 24, 2026 Glazyrin, Konstantin; Spektor, Kristina; Bykov, Maxim; Brant Carvalho, Paulo Henrique; Dong, Weiwei; Körmann, Fritz; Sano-Furukawa, Asami; Hattori, Takanori; Beyer, Doreen; Sahlberg, Martin; Ikeda, Yuji; Yu, Ji-Hun; Yang, Sangsun; Lee, Jai-sung; Bhat, Shrikant; Michael, Hanfland; Grabowski, Blazej; Divinski, Sergiy; Yusenko, Kirill, 2026, "Replication Data for: Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions", https://doi.org/10.18419/DARUS-5698, DaRUS, V2 Data for reproducing DFT simulations in the manuscript "Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions". The data contain the optimized atomic positions of the following systems in the VASP POSCAR/CONTCAR format. fcc_reference: 5 SQS models x 9 volumes in FCC, without H fcc_all_octa: 5 SQS models x 16...
Data for: Hydrogen diffusion in TiCr2Hx Laves phases: A combined ab initio and machine-learning-potential study Feb 20, 2026 Kumar, Pranav; Körmann, Fritz; Edalati, Kaveh; Grabowski, Blazej; Ikeda, Yuji, 2026, "Data for: Hydrogen diffusion in TiCr2Hx Laves phases: A combined ab initio and machine-learning-potential study", https://doi.org/10.18419/DARUS-5544, DaRUS, V1 This dataset supports the development and validation of machine learning interatomic potentials (MLIPs) for modeling hydrogen diffusion in C14 (hexagonal) and C15 (cubic) TiCr₂-based Laves phases. It includes fitted moment tensor potentials (Level 16) and the corresponding training dataset. The data is organized into two directories: Training_db/,...
Data for: Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs Dec 17, 2025 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 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 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 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 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 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 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: Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions

Feb 24, 2026

Ikeda, Yuji; Forslund, Axel; Kumar, Pranav; Ou, Yongliang; Jung, Jong Hyun; Koehn, Andreas; Grabowski, Blazej, 2026, "Data for: Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions", https://doi.org/10.18419/DARUS-5272, DaRUS, V1

Data for reproducing the results in the manuscript "Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions". Find further details in README.md.

Replication Data for: Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions

Feb 24, 2026

Glazyrin, Konstantin; Spektor, Kristina; Bykov, Maxim; Brant Carvalho, Paulo Henrique; Dong, Weiwei; Körmann, Fritz; Sano-Furukawa, Asami; Hattori, Takanori; Beyer, Doreen; Sahlberg, Martin; Ikeda, Yuji; Yu, Ji-Hun; Yang, Sangsun; Lee, Jai-sung; Bhat, Shrikant; Michael, Hanfland; Grabowski, Blazej; Divinski, Sergiy; Yusenko, Kirill, 2026, "Replication Data for: Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions", https://doi.org/10.18419/DARUS-5698, DaRUS, V2

Data for reproducing DFT simulations in the manuscript "Synthesis of High-Entropy Hydride from the Cantor Alloy (fcc-CoCrFeNiMn) at Extreme Conditions". The data contain the optimized atomic positions of the following systems in the VASP POSCAR/CONTCAR format. fcc_reference: 5 SQS models x 9 volumes in FCC, without H fcc_all_octa: 5 SQS models x 16...

Data for: Hydrogen diffusion in TiCr2Hx Laves phases: A combined ab initio and machine-learning-potential study

Feb 20, 2026

Kumar, Pranav; Körmann, Fritz; Edalati, Kaveh; Grabowski, Blazej; Ikeda, Yuji, 2026, "Data for: Hydrogen diffusion in TiCr2Hx Laves phases: A combined ab initio and machine-learning-potential study", https://doi.org/10.18419/DARUS-5544, DaRUS, V1

This dataset supports the development and validation of machine learning interatomic potentials (MLIPs) for modeling hydrogen diffusion in C14 (hexagonal) and C15 (cubic) TiCr₂-based Laves phases. It includes fitted moment tensor potentials (Level 16) and the corresponding training dataset. The data is organized into two directories: Training_db/,...

Data for: Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs

Dec 17, 2025

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

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

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

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

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

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

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...

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