Zobrazeno 1 - 10
of 128
pro vyhledávání: '"Jörg Behler"'
Autor:
Regine Herbst-Irmer, Xiaobai Wang, Laura Haberstock, Ingo Köhne, Rainer Oswald, Jörg Behler, Dietmar Stalke
Publikováno v:
IUCrJ, Vol 10, Iss 6, Pp 766-771 (2023)
Phosphorus exists in several different allotropes: white, red, violet and black. For industrial and academic applications, white phosphorus is the most important. So far, three polymorphs of white phosphorus, all consisting of P4 tetrahedra, have bee
Externí odkaz:
https://doaj.org/article/5b5d2b647e134fe69d0ba6371fd2662c
Autor:
Marco Eckhoff, Jörg Behler
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)
Abstract Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first-principles quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin arrangements and t
Externí odkaz:
https://doaj.org/article/4bc512798dfb4956ba29a6cae7102e9d
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Machine learning potentials do not account for long-range charge transfer. Here the authors introduce a fourth-generation high-dimensional neural network potential including non-local information of charge populations that is able to provide forces,
Externí odkaz:
https://doaj.org/article/f40349819e214240bffe571e08cfbe15
Autor:
Jörg Behler
Publikováno v:
IST International Surface Technology. 16:38-41
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
Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::713e6b9b7814b7c0664bfe9814dc8765
https://doi.org/10.26434/chemrxiv-2023-rskz3
https://doi.org/10.26434/chemrxiv-2023-rskz3
Publikováno v:
Annual Review of Physical Chemistry. 73:163-186
In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of systems in chemistry, physics, and materials science. Different machine
Publikováno v:
The Journal of Physical Chemistry C. 125:14897-14909
Publikováno v:
Physical review letters. 129(22)
Coupled cluster theory is a general and systematic electronic structure method, but in particular the highly accurate "gold standard" coupled cluster singles, doubles and perturbative triples, CCSD(T), can only be applied to small systems. To overcom