EXC 2075 Project Networks

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Subject: Chemistry

1 to 10 of 27 Results

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-potentia...
Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants." Jul 18, 2023 - BLinK Schlaich, Alexander, 2023, "Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants."", https://doi.org/10.18419/darus-2360, DaRUS, V1 Simulation input scripts to produce the data presented in the manuscript "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants." All simulations were carried out using the GROMACS simulation package. The folders contain...
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...
PN 3-10(Universität Stuttgart) Jun 12, 2023PN 3 We aim to use first-principles calculations at finite temperatures in combination with machine learning (ML) techniques to derive an accurate picture of hydrogen embrittlement in Ni-based superalloys. The ML-based interatomic potential will allow for the determination of the temp...
BLinK(Universität Stuttgart) Jun 7, 2023Membranes Container for the BLinK project
Membranes(Universität Stuttgart) Jun 7, 2023PN 1-8 Dataverse containig material on lipid membranes and surfaces interactions
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 f...
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 (VAS...
Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Feb 20, 2023 - PN 6 Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1 Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint...
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 mom...

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-potentia...

Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants."

Jul 18, 2023 - BLinK

Schlaich, Alexander, 2023, "Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants."", https://doi.org/10.18419/darus-2360, DaRUS, V1

Simulation input scripts to produce the data presented in the manuscript "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants." All simulations were carried out using the GROMACS simulation package. The folders contain...

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...

PN 3-10(Universität Stuttgart)

Jun 12, 2023PN 3

We aim to use first-principles calculations at finite temperatures in combination with machine learning (ML) techniques to derive an accurate picture of hydrogen embrittlement in Ni-based superalloys. The ML-based interatomic potential will allow for the determination of the temp...

BLinK(Universität Stuttgart)

Jun 7, 2023Membranes

Container for the BLinK project

Membranes(Universität Stuttgart)

Jun 7, 2023PN 1-8

Dataverse containig material on lipid membranes and surfaces interactions

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 f...

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 (VAS...

Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Feb 20, 2023 - PN 6

Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1

Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint...

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 mom...

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