PN 3

SimTech EXC 2075 Project Network 3 "Data-integrated model reduction for particles and continua"

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1 to 10 of 19 Results

2d microstructure data Feb 15, 2021 - Data Analytics in Engineering Lißner, Julian, 2020, "2d microstructure data", https://doi.org/10.18419/darus-1151, DaRUS, V2 The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions. 30.000 images are contained and split into...
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...
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: 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...
PN 3-1(Universität Stuttgart) Feb 15, 2021 Processing Uncertain Microstructural Data: The projects objective is to relate image-based data of stochastic microstructures to their quantities of interest. The data consists mainly of microstructure images, corresponding material properties and features deployed in machine lea...
PN 3-10(Universität Stuttgart) Jun 12, 2023 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...
PN 3-11(Universität Stuttgart) Feb 28, 2023 Biological Molecular Dynamics Simulations
PN 3-8(Universität Stuttgart) Jul 5, 2022 Use of physically-based surrogate models to accelerate the optimization of classical force fields and development of new reduced order models for transport properties based on entropy scaling. Machine-learned models for transport properties will be developed with an increasing da...
PN 3A-7(Universität Stuttgart) Mar 8, 2024 Advanced Learning Strategies for Machine Learned Interatomic Potentials

2d microstructure data

Feb 15, 2021 - Data Analytics in Engineering

Lißner, Julian, 2020, "2d microstructure data", https://doi.org/10.18419/darus-1151, DaRUS, V2

The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions. 30.000 images are contained and split into...

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...

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: 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...

PN 3-1(Universität Stuttgart)

Feb 15, 2021

Processing Uncertain Microstructural Data: The projects objective is to relate image-based data of stochastic microstructures to their quantities of interest. The data consists mainly of microstructure images, corresponding material properties and features deployed in machine lea...

PN 3-10(Universität Stuttgart)

Jun 12, 2023

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...

PN 3-11(Universität Stuttgart)

Feb 28, 2023

Biological Molecular Dynamics Simulations

PN 3-8(Universität Stuttgart)

Jul 5, 2022

Use of physically-based surrogate models to accelerate the optimization of classical force fields and development of new reduced order models for transport properties based on entropy scaling. Machine-learned models for transport properties will be developed with an increasing da...

PN 3A-7(Universität Stuttgart)

Mar 8, 2024

Advanced Learning Strategies for Machine Learned Interatomic Potentials

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