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1 to 4 of 4 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...
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...
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 for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
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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