Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Jonas A. Finkler"'
Autor:
Marco Krummenacher, Moritz Gubler, Jonas A. Finkler, Hannes Huber, Martin Sommer-Jörgensen, Stefan Goedecker
Publikováno v:
SoftwareX, Vol 26, Iss , Pp 101645- (2024)
Externí odkaz:
https://doaj.org/article/f779249c84d0420590cd740eb49c6c0f
Autor:
Marco Krummenacher, Moritz Gubler, Jonas A. Finkler, Hannes Huber, Martin Sommer-Jörgensen, Stefan Goedecker
Publikováno v:
SoftwareX, Vol 25, Iss , Pp 101632- (2024)
In materials science, the quest to find stable low energy structures is of great significance. Over the past two decades, the Minima Hopping algorithm has emerged as a successful tool in this pursuit. We present a robust, user friendly and efficient
Externí odkaz:
https://doaj.org/article/92f0718cf922488fb6dc3fa115c57bb1
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
Publikováno v:
Condensed Matter, Vol 6, Iss 1, p 9 (2021)
Using fingerprints used mainly in machine learning schemes of the potential energy surface, we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long ran
Externí odkaz:
https://doaj.org/article/06c8fbf1e747404e91a1ffa20eb19a88
Autor:
Jonas A. Finkler, Stefan Goedecker
Publikováno v:
Materials Advances. 4:184-194
Funnel Hopping Monte Carlo simulations of MaPbI3 show that the delta phases which have a lower energy than the perovskite phases are only thermodynamically preferred up to 200 K. This explains the absence of the delta phases in experiments.
Publikováno v:
Materials Advances. 4:1746-1768
We unveil the principles of isomer stability in small clusters. Our conclusions are based on a extensive statistical analysis of various structural and electronic descriptors on a huge database of isomers generated by ab-initio structure predictions.
Publikováno v:
Physical Review B. 105
Publikováno v:
Accounts of chemical research. 54(4)
The development of first-principles-quality machine learning potentials (MLP) has seen tremendous progress, now enabling computer simulations of complex systems for which sufficiently accurate interatomic potentials have not been available. These adv
Autor:
Jonas A. Finkler, Stefan Goedecker
Monte Carlo simulations are a powerful tool to investigate the thermodynamic properties of atomic systems. In practice however, sampling of the complete configuration space is often hindered by high energy barriers between different regions of config
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a2e6c5f00df24e7af96a73835727ddd
Publikováno v:
Condensed Matter
Volume 6
Issue 1
Condensed Matter, Vol 6, Iss 9, p 9 (2021)
Volume 6
Issue 1
Condensed Matter, Vol 6, Iss 9, p 9 (2021)
Using fingerprints used mainly in machine learning schemes of the potential energy surface, we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long ran