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Autor:
Marius Herbold, Jörg Behler
Publikováno v:
Physical Chemistry Chemical Physics. 25:12979-12989
Molecular fragments of metal–organic frameworks can be used to construct high-dimensional neural network potentials. Here we provide a recipe of how the smallest possible fragments can be chosen that still provide a HDNNP transferable to the bulk c
Autor:
Marius Herbold, Jörg Behler
In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PES) with close to first-principles accuracy. Most current MLPs rely on atomic energy cont
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf1059dd4b7f4a4fa89f560b9033ff31