<div class="csl-bib-body">
<div class="csl-entry">Schörghuber, J., Carrete Montana, J., & Madsen, G. K. H. (2023). Electron‐Passing Neural Networks. In <i>Joint TACO-NanoCat Conference 2023:TAming COmplexity in Materials: Synergies between Experiment and Modeling: Program and Book of Abstracts</i>. Joint TACO-NanoCat Conference 2023: TAming COmplexity in Materials: Synergies between Experiment and Modeling, Wien, Austria.</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/194638
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dc.description.abstract
While neural-network force fields based on the Behler-Parrinello (BP) architecture already make the construction of highly accurate and fast-to-evaluate potential energy hypersurface representations possible, there still remain open challenges. Among these is the inclusion of long-range interactions and the modelling of forces due to externally applied electric fields. We present an investigation of electron-passing neural networks (EPNN) as an option to tackle these challenges. Emphasis is put on finding physically well-defined target quantities for prediction.
en
dc.language.iso
en
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dc.subject
Machine Learning
en
dc.subject
Modelling
en
dc.subject
Neural Networks
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dc.title
Electron‐Passing Neural Networks
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.type.category
Poster Contribution
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tuw.booktitle
Joint TACO-NanoCat Conference 2023:TAming COmplexity in Materials: Synergies between Experiment and Modeling: Program and Book of Abstracts
