Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning.
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%0 Journal Article
%1 journals/corr/abs-2311-00787
%A Ward, Logan T.
%A Blaiszik, Ben
%A Lee, Cheng-Wei
%A Martin, Troy
%A Foster, Ian T.
%A Schleife, André
%D 2023
%J CoRR
%K dblp
%T Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning.
%U http://dblp.uni-trier.de/db/journals/corr/corr2311.html#abs-2311-00787
%V abs/2311.00787
@article{journals/corr/abs-2311-00787,
added-at = {2023-11-23T00:00:00.000+0100},
author = {Ward, Logan T. and Blaiszik, Ben and Lee, Cheng-Wei and Martin, Troy and Foster, Ian T. and Schleife, André},
biburl = {https://www.bibsonomy.org/bibtex/242db212e334428c8590e288f9c9efedc/dblp},
ee = {https://doi.org/10.48550/arXiv.2311.00787},
interhash = {c20e9d95aeb1391c08d350aa45299928},
intrahash = {42db212e334428c8590e288f9c9efedc},
journal = {CoRR},
keywords = {dblp},
timestamp = {2024-04-09T00:26:34.000+0200},
title = {Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning.},
url = {http://dblp.uni-trier.de/db/journals/corr/corr2311.html#abs-2311-00787},
volume = {abs/2311.00787},
year = 2023
}
