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Subject: Chemistry Funding Information Identifier: EXC 2075 - 390740016

11 to 20 of 32 Results

Scripts for "Assessing the validity of NMR relaxation rates obtained from coarse-grained simulations" Mar 13, 2023 - Holm group Gravelle, Simon; Beyer, David; Brito, Mariano E.; Schlaich, Alexander; Holm, Christian, 2023, "Scripts for "Assessing the validity of NMR relaxation rates obtained from coarse-grained simulations"", https://doi.org/10.18419/darus-3313, DaRUS, V1 Simulations and data analysis scripts for the publication "Assessing the validity of NMR relaxation rates obtained from coarse-grained simulations". The dataset contains two types of simulation scripts: all-atom simulation (GROMACS) and coarse-grained simulations (ESPRESSO). In b...
Molecular simulation scripts for bulk solutions Mar 8, 2023 - NMR investigation of water confined by salt interface Gravelle, Simon; Holm, Christian; Schlaich, Alexander, 2023, "Molecular simulation scripts for bulk solutions", https://doi.org/10.18419/darus-3179, DaRUS, V1 GROMACS molecular simulation input files for bulk solutions of NaCl and Na2SO4. Initial configuration with different salt concentration can be generated using the Python script ConfigurationGenerator.py, and successive GROMACS runs can be performed by running the runall.sh Bash s...
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...
Self-diffusion coefficients of simulated aqueous glycerol mixtures Oct 28, 2022 - ThermoML Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Self-diffusion coefficients of simulated aqueous glycerol mixtures", https://doi.org/10.18419/darus-3115, DaRUS, V1 In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experi...
Densities of simulated aqueous methanol mixtures Oct 28, 2022 - ThermoML Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of simulated aqueous methanol mixtures", https://doi.org/10.18419/darus-3112, DaRUS, V1 In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experi...
Self-diffusion coefficients of simulated aqueous methanol mixtures Oct 28, 2022 - ThermoML Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Self-diffusion coefficients of simulated aqueous methanol mixtures", https://doi.org/10.18419/darus-3114, DaRUS, V1 In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experi...
Densities of simulated aqueous glycerol mixtures Oct 28, 2022 - ThermoML Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of simulated aqueous glycerol mixtures", https://doi.org/10.18419/darus-3113, DaRUS, V1 In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experi...
Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state' Aug 26, 2022 - Institute of Thermodynamics and Thermal Process Engineering Zimmermann, Nils Edvin Richard; Guevara-Carrion, Gabriela; Vrabec, Jadran; Hansen, Niels, 2022, "Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state'", https://doi.org/10.18419/darus-2996, DaRUS, V2 Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state' (N. E. R. Zimmermann, G. Guevara-Carrion, J. Vrabec, N. Hansen, Adv. Theory Simul., 2022) containing scripts, packages, and files to re-create and...
Enzymatically induced carbonate precipitation and its effect on capillary pressure-saturation relations of porous media - micro-XRCT dataset of low column (sample 10) Jul 12, 2022 - Publication: Enzymatically induced carbonate precipitation Ruf, Matthias; Hommel, Johannes; Steeb, Holger, 2022, "Enzymatically induced carbonate precipitation and its effect on capillary pressure-saturation relations of porous media - micro-XRCT dataset of low column (sample 10)", https://doi.org/10.18419/darus-2908, DaRUS, V1, UNF:6:lD3UlWjZQRaGPURMuWejpw== [fileUNF] This dataset contains a micro X-ray Computed Tomography (micro-XRCT) data set (projection, reconstructed, and segmented images) from a sintered glass beads column packing with enzymatically induced carbonate precipitation. The prepared sintered glass beads column sample has a dia...

Scripts for "Assessing the validity of NMR relaxation rates obtained from coarse-grained simulations"

Mar 13, 2023 - Holm group

Gravelle, Simon; Beyer, David; Brito, Mariano E.; Schlaich, Alexander; Holm, Christian, 2023, "Scripts for "Assessing the validity of NMR relaxation rates obtained from coarse-grained simulations"", https://doi.org/10.18419/darus-3313, DaRUS, V1

Simulations and data analysis scripts for the publication "Assessing the validity of NMR relaxation rates obtained from coarse-grained simulations". The dataset contains two types of simulation scripts: all-atom simulation (GROMACS) and coarse-grained simulations (ESPRESSO). In b...

Molecular simulation scripts for bulk solutions

Mar 8, 2023 - NMR investigation of water confined by salt interface

Gravelle, Simon; Holm, Christian; Schlaich, Alexander, 2023, "Molecular simulation scripts for bulk solutions", https://doi.org/10.18419/darus-3179, DaRUS, V1

GROMACS molecular simulation input files for bulk solutions of NaCl and Na2SO4. Initial configuration with different salt concentration can be generated using the Python script ConfigurationGenerator.py, and successive GROMACS runs can be performed by running the runall.sh Bash s...

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...

Self-diffusion coefficients of simulated aqueous glycerol mixtures

Oct 28, 2022 - ThermoML

Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Self-diffusion coefficients of simulated aqueous glycerol mixtures", https://doi.org/10.18419/darus-3115, DaRUS, V1

In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experi...

Densities of simulated aqueous methanol mixtures

Oct 28, 2022 - ThermoML

Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of simulated aqueous methanol mixtures", https://doi.org/10.18419/darus-3112, DaRUS, V1

Self-diffusion coefficients of simulated aqueous methanol mixtures

Oct 28, 2022 - ThermoML

Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Self-diffusion coefficients of simulated aqueous methanol mixtures", https://doi.org/10.18419/darus-3114, DaRUS, V1

Densities of simulated aqueous glycerol mixtures

Oct 28, 2022 - ThermoML

Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of simulated aqueous glycerol mixtures", https://doi.org/10.18419/darus-3113, DaRUS, V1

Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state'

Aug 26, 2022 - Institute of Thermodynamics and Thermal Process Engineering

Zimmermann, Nils Edvin Richard; Guevara-Carrion, Gabriela; Vrabec, Jadran; Hansen, Niels, 2022, "Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state'", https://doi.org/10.18419/darus-2996, DaRUS, V2

Supplementary material for 'Predicting and rationalizing the Soret coefficient of binary Lennard-Jones mixtures in the liquid state' (N. E. R. Zimmermann, G. Guevara-Carrion, J. Vrabec, N. Hansen, Adv. Theory Simul., 2022) containing scripts, packages, and files to re-create and...

Enzymatically induced carbonate precipitation and its effect on capillary pressure-saturation relations of porous media - micro-XRCT dataset of low column (sample 10)

Jul 12, 2022 - Publication: Enzymatically induced carbonate precipitation

Ruf, Matthias; Hommel, Johannes; Steeb, Holger, 2022, "Enzymatically induced carbonate precipitation and its effect on capillary pressure-saturation relations of porous media - micro-XRCT dataset of low column (sample 10)", https://doi.org/10.18419/darus-2908, DaRUS, V1, UNF:6:lD3UlWjZQRaGPURMuWejpw== [fileUNF]

This dataset contains a micro X-ray Computed Tomography (micro-XRCT) data set (projection, reconstructed, and segmented images) from a sintered glass beads column packing with enzymatically induced carbonate precipitation. The prepared sintered glass beads column sample has a dia...

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