Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Emir Kocer"'
Publikováno v:
AIP Advances, Vol 10, Iss 1, Pp 015021-015021-7 (2020)
Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of electronic structure methods with the speed of classical interatomic potentials. A crucial component of a machine learning potential is the descrip
Externí odkaz:
https://doaj.org/article/5ff188d6a30e40d3bdbceaca3378bc4e
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
An assessment of the structural resolution of various fingerprints commonly used in machine learning
Autor:
Jörg Behler, Sandip De, Emir Kocer, Anatole von Lilienfeld, Stefan Goedecker, Anders S. Christensen, Deb Sankar De, Felix A. Faber, Behnam Parsaeifard
Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2716e6d63de761d5db78de409181f0d9
A central concern of molecular dynamics simulations are the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system, and have generally been calculated using either predefined analytica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b40cab0783216cb2af8ed9908c944ca
http://arxiv.org/abs/1907.02374
http://arxiv.org/abs/1907.02374
Publikováno v:
AIP Advances, Vol 10, Iss 1, Pp 015021-015021-7 (2020)
Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of quantum mechanical simulations with the speed of classical interatomic potentials. A crucial component of a machine learning potential is the descr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::793147eeb44acd46d1df5e7b278e289c
Publikováno v:
Materials Today Communications. 20:100533
Thermal transport in a water–Cu nanocolloid system was investigated using equilibrium molecular dynamics. A systematic analysis of the Green–Kubo calculations is presented to clarify the effect of simulation parameters. Several sources of error w