Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials
V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner. Dataset, (2023)Related to: Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. doi: 10.1039/D2CP05793J.
DOI: 10.18419/darus-3299
Abstract
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 checkpoints and stores the metadata for the checkpoint and parameters for the model. Checkpoints can be read by the source code provided on GitLab. A detailed description for reproducing the results and employing pre-trained and fine-tuned models during a simulation is provided in the GM-NN Documentation.
Related to: Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. doi: 10.1039/D2CP05793J
%0 Generic
%1 zaverkin2023pretrained
%A Zaverkin, Viktor
%A Holzmüller, David
%A Bonfirraro, Luca
%A Kästner, Johannes
%D 2023
%K darus mult ubs_10003 ubs_10008 ubs_10021 ubs_20003 ubs_20013 ubs_20019 ubs_30039 ubs_30126 ubs_30165 ubs_40065 unibibliografie
%R 10.18419/darus-3299
%T Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials
%X 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 checkpoints and stores the metadata for the checkpoint and parameters for the model. Checkpoints can be read by the source code provided on GitLab. A detailed description for reproducing the results and employing pre-trained and fine-tuned models during a simulation is provided in the GM-NN Documentation.
@misc{zaverkin2023pretrained,
abstract = {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 checkpoints and stores the metadata for the checkpoint and parameters for the model. Checkpoints can be read by the source code provided on GitLab. A detailed description for reproducing the results and employing pre-trained and fine-tuned models during a simulation is provided in the GM-NN Documentation. },
added-at = {2023-02-27T07:58:16.000+0100},
affiliation = {Zaverkin, Viktor/Universität Stuttgart, Holzmüller, David/Universität Stuttgart, Bonfirraro, Luca/Universität Stuttgart, Kästner, Johannes/Universität Stuttgart},
author = {Zaverkin, Viktor and Holzmüller, David and Bonfirraro, Luca and Kästner, Johannes},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2a8f6962137b231bbba6b4b949abc17cd/unibiblio},
doi = {10.18419/darus-3299},
howpublished = {Dataset},
interhash = {ca09e8cf904f081df27bf7cd6d3ec935},
intrahash = {a8f6962137b231bbba6b4b949abc17cd},
keywords = {darus mult ubs_10003 ubs_10008 ubs_10021 ubs_20003 ubs_20013 ubs_20019 ubs_30039 ubs_30126 ubs_30165 ubs_40065 unibibliografie},
note = {Related to: Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. doi: 10.1039/D2CP05793J},
orcid-numbers = {Zaverkin, Viktor/0000-0001-9940-8548, Holzmüller, David/0000-0002-9443-0049, Bonfirraro, Luca/0000-0003-4799-2986, Kästner, Johannes/0000-0001-6178-7669},
timestamp = {2023-02-27T07:58:16.000+0100},
title = {Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials},
year = 2023
}
