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Subject: Chemistry License: MIT License

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Publication data for: "The presence of a wall enhances the probability for ring-closing metathesis: insights from classical polymer theory and atomistic simulations - data from C6 group" Jul 20, 2022 - SFB 1333 C6 - Holm group, ICP Tischler, Ingo; Holm, Christian; Schlaich, Alexander, 2022, "Publication data for: "The presence of a wall enhances the probability for ring-closing metathesis: insights from classical polymer theory and atomistic simulations - data from C6 group"", https://doi.org/10.18419/darus-1371, DaRUS, V1 All primary data files and processed data of the journal article from C6 group. It contains the simulation and analysis scripts, the ESPResSo commit and a patch to it, with which the data was produced.
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

Publication data for: "The presence of a wall enhances the probability for ring-closing metathesis: insights from classical polymer theory and atomistic simulations - data from C6 group"

Jul 20, 2022 - SFB 1333 C6 - Holm group, ICP

Tischler, Ingo; Holm, Christian; Schlaich, Alexander, 2022, "Publication data for: "The presence of a wall enhances the probability for ring-closing metathesis: insights from classical polymer theory and atomistic simulations - data from C6 group"", https://doi.org/10.18419/darus-1371, DaRUS, V1

All primary data files and processed data of the journal article from C6 group. It contains the simulation and analysis scripts, the ESPResSo commit and a patch to it, with which the data was produced.

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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