Representing molecule-surface interactions with symmetry-adapted neural networks. Behler, J., Lorenz, S., & Reuter, K. The Journal of Chemical Physics, 127(1):014705, July, 2007. 00047![Representing molecule-surface interactions with symmetry-adapted neural networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energysurface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.
@article{ behler_representing_2007,
title = {Representing molecule-surface interactions with symmetry-adapted neural networks},
volume = {127},
issn = {0021-9606, 1089-7690},
url = {http://scitation.aip.org/content/aip/journal/jcp/127/1/10.1063/1.2746232},
doi = {10.1063/1.2746232},
abstract = {The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energysurface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.},
number = {1},
urldate = {2014-06-05TZ},
journal = {The Journal of Chemical Physics},
author = {Behler, Jörg and Lorenz, Sönke and Reuter, Karsten},
month = {July},
year = {2007},
note = {00047},
keywords = {crystal, neural-networks, reading},
pages = {014705}
}
Downloads: 0
{"_id":"4xZefEa359b2EMuRK","bibbaseid":"behler-lorenz-reuter-representingmoleculesurfaceinteractionswithsymmetryadaptedneuralnetworks-2007","downloads":0,"creationDate":"2015-09-03T07:16:37.384Z","title":"Representing molecule-surface interactions with symmetry-adapted neural networks","author_short":["Behler, J.","Lorenz, S.","Reuter, K."],"year":2007,"bibtype":"article","biburl":"http://bibbase.org/zotero/fred.qi","bibdata":{"abstract":"The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energysurface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.","author":["Behler, Jörg","Lorenz, Sönke","Reuter, Karsten"],"author_short":["Behler, J.","Lorenz, S.","Reuter, K."],"bibtex":"@article{ behler_representing_2007,\n title = {Representing molecule-surface interactions with symmetry-adapted neural networks},\n volume = {127},\n issn = {0021-9606, 1089-7690},\n url = {http://scitation.aip.org/content/aip/journal/jcp/127/1/10.1063/1.2746232},\n doi = {10.1063/1.2746232},\n abstract = {The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energysurface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.},\n number = {1},\n urldate = {2014-06-05TZ},\n journal = {The Journal of Chemical Physics},\n author = {Behler, Jörg and Lorenz, Sönke and Reuter, Karsten},\n month = {July},\n year = {2007},\n note = {00047},\n keywords = {crystal, neural-networks, reading},\n pages = {014705}\n}","bibtype":"article","doi":"10.1063/1.2746232","id":"behler_representing_2007","issn":"0021-9606, 1089-7690","journal":"The Journal of Chemical Physics","key":"behler_representing_2007","keywords":"crystal, neural-networks, reading","month":"July","note":"00047","number":"1","pages":"014705","title":"Representing molecule-surface interactions with symmetry-adapted neural networks","type":"article","url":"http://scitation.aip.org/content/aip/journal/jcp/127/1/10.1063/1.2746232","urldate":"2014-06-05TZ","volume":"127","year":"2007","bibbaseid":"behler-lorenz-reuter-representingmoleculesurfaceinteractionswithsymmetryadaptedneuralnetworks-2007","role":"author","urls":{"Paper":"http://scitation.aip.org/content/aip/journal/jcp/127/1/10.1063/1.2746232"},"keyword":["crystal","neural-networks","reading"],"downloads":0},"search_terms":["representing","molecule","surface","interactions","symmetry","adapted","neural","networks","behler","lorenz","reuter"],"keywords":["crystal","neural-networks","reading"],"authorIDs":[],"dataSources":["y8en6y5RHukfeLuPH"]}