Stuttgart Center for Simulation Science (SC SimTech)

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Author Name: Kästner, Johannes

1 to 7 of 7 Results

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...
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Apr 5, 2023 - PN 6 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2023, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]", https://doi.org/10.18419/darus-3394, DaRUS, V1 This dataset contains code and data for the third arXiv version of our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Act...
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...
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2] Aug 24, 2022 - PN 6 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]", https://doi.org/10.18419/darus-3110, DaRUS, V1 This dataset contains code and data for the second arXiv version of our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Ac...
Supplementary material for 'Influence of Layer Slipping on Adsorption of Light Gases in Covalent Organic Frameworks: A Combined Experimental and Computational Study' Apr 26, 2022 - Institute of Thermodynamics and Thermal Process Engineering Kessler, Christopher; Schuldt, Robin; Emmerling, Sebastian; Lotsch, Bettina; Kästner, Johannes; Gross, Joachim; Hansen, Niels, 2022, "Supplementary material for 'Influence of Layer Slipping on Adsorption of Light Gases in Covalent Organic Frameworks: A Combined Experimental and Computational Study'", https://doi.org/10.18419/darus-2308, DaRUS, V1, UNF:6:ifmtNZEZHi+MkSvB5rd1dw== [fileUNF] This dataset contains results from Grand Canonical Monte Carlo (GCMC) Simulation (data/isotherms_sim/) and experiment (data/isotherms/exp). All Data is presented in a jupyter notebook and for a fast overview without executing the notebook also as pdf-file. Furthermore the dataset...
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Apr 13, 2022 - PN 6 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]", https://doi.org/10.18419/darus-2615, DaRUS, V1 This dataset contains code and data for our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Active Learning problems. The...
Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Oct 15, 2021 - PN 6 Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1 Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

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...

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Apr 5, 2023 - PN 6

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2023, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]", https://doi.org/10.18419/darus-3394, DaRUS, V1

This dataset contains code and data for the third arXiv version of our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Act...

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...

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]

Aug 24, 2022 - PN 6

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]", https://doi.org/10.18419/darus-3110, DaRUS, V1

This dataset contains code and data for the second arXiv version of our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Ac...

Supplementary material for 'Influence of Layer Slipping on Adsorption of Light Gases in Covalent Organic Frameworks: A Combined Experimental and Computational Study'

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

Kessler, Christopher; Schuldt, Robin; Emmerling, Sebastian; Lotsch, Bettina; Kästner, Johannes; Gross, Joachim; Hansen, Niels, 2022, "Supplementary material for 'Influence of Layer Slipping on Adsorption of Light Gases in Covalent Organic Frameworks: A Combined Experimental and Computational Study'", https://doi.org/10.18419/darus-2308, DaRUS, V1, UNF:6:ifmtNZEZHi+MkSvB5rd1dw== [fileUNF]

This dataset contains results from Grand Canonical Monte Carlo (GCMC) Simulation (data/isotherms_sim/) and experiment (data/isotherms/exp). All Data is presented in a jupyter notebook and for a fast overview without executing the notebook also as pdf-file. Furthermore the dataset...

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Apr 13, 2022 - PN 6

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]", https://doi.org/10.18419/darus-2615, DaRUS, V1

This dataset contains code and data for our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Active Learning problems. The...

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Oct 15, 2021 - PN 6

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

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