Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Stefano de Gironcoli"'
Publikováno v:
npj 2D Materials and Applications, Vol 8, Iss 1, Pp 1-13 (2024)
Abstract Surface plasmon polaritons (SPPs), which are electromagnetic modes representing collective oscillations of charge density coupled with photons, have been extensively studied in graphene. This has provided a solid foundation for understanding
Externí odkaz:
https://doaj.org/article/a5dc6de2bc8c459bb53c83992824e8a7
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-12 (2024)
Abstract We present a new approach to construct machine-learned interatomic potentials including long-range electrostatic interactions based on a charge equilibration scheme. This new approach can accurately describe the potential energy surface of s
Externí odkaz:
https://doaj.org/article/95859c91c7874883ad3a79f2050fb6d5
Autor:
Claudio Zeni, Kevin Rossi, Theodore Pavloudis, Joseph Kioseoglou, Stefano de Gironcoli, Richard E. Palmer, Francesca Baletto
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Efficient theoretical methods for the structural analysis of nanoparticles are very much needed. Here the authors demonstrate the use of machine-learning force fields and of a data-driven approach to study the thermodynamical stability and elucidate
Externí odkaz:
https://doaj.org/article/7082f3d6e6ca4eeb96a89bcbac54c56b
Autor:
Yusuf Shaidu, Emine Küçükbenli, Ruggero Lot, Franco Pellegrini, Efthimios Kaxiras, Stefano de Gironcoli
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-13 (2021)
Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural
Externí odkaz:
https://doaj.org/article/3615bdf517374055a6e295aa00227d24
Autor:
Craig L. Bull, Giles Flowitt-Hill, Stefano de Gironcoli, Emine Küçükbenli, Simon Parsons, Cong Huy Pham, Helen Y. Playford, Matthew G. Tucker
Publikováno v:
IUCrJ, Vol 4, Iss 5, Pp 569-574 (2017)
Glycine is the simplest and most polymorphic amino acid, with five phases having been structurally characterized at atmospheric or high pressure. A sixth form, the elusive ζ phase, was discovered over a decade ago as a short-lived intermediate which
Externí odkaz:
https://doaj.org/article/3970d1e1d7f0456c916db810e47a3ae5
Autor:
Antoine Jay, Miha Gunde, Nicolas Salles, Matic Poberžnik, Layla Martin-Samos, Nicolas Richard, Stefano de Gironcoli, Normand Mousseau, Anne Hémeryck
Publikováno v:
Computational Materials Science
Computational Materials Science, 2022, 209, pp.111363. ⟨10.1016/j.commatsci.2022.111363⟩
Computational Materials Science, 2022, 209, pp.111363. ⟨10.1016/j.commatsci.2022.111363⟩
International audience; The Activation–Relaxation Technique (ARTn) is an efficient technique for finding the minima and saddle points of multidimensional functions such as the potential energy surface of atomic systems in chemistry. In this work we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38fee38d8348661ce7c739f19b6c1b94
https://hal.laas.fr/hal-03640786
https://hal.laas.fr/hal-03640786
Charge transfer from a metal substrate stabilizes honeycomb borophene, whose electron deficit would otherwise spoil the hexagonal order of a π-bonded two-dimensional (2D) atomic network. However, the coupling between the substrate and the boron over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::742116595d3ed919cd96179277e4c3e2
Publikováno v:
16th IEEE Nanotechnology Materials and Devices Conference (IEEE NMDC 2021)
16th IEEE Nanotechnology Materials and Devices Conference (IEEE NMDC 2021), Dec 2021, Vancouver, Canada. ⟨10.1109/NMDC50713.2021.9677541⟩
2021 IEEE 16th Nanotechnology Materials and Devices Conference (NMDC)
16th IEEE Nanotechnology Materials and Devices Conference (IEEE NMDC 2021), Dec 2021, Vancouver, Canada
16th IEEE Nanotechnology Materials and Devices Conference, NMDC 2021Vancouver proceedings
16th IEEE Nanotechnology Materials and Devices Conference (IEEE NMDC 2021), Dec 2021, Vancouver, Canada. ⟨10.1109/NMDC50713.2021.9677541⟩
2021 IEEE 16th Nanotechnology Materials and Devices Conference (NMDC)
16th IEEE Nanotechnology Materials and Devices Conference (IEEE NMDC 2021), Dec 2021, Vancouver, Canada
16th IEEE Nanotechnology Materials and Devices Conference, NMDC 2021Vancouver proceedings
Prix du meilleur article étudiant (https://www.laas.fr/public/fr/node/6965); International audience; In this work, we develop a new neural network potential for silicon and perform accurate molecular dynamics simulations of the liquid, amorphous and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::610de54794bcd9a91fff4033f8c57eb6
https://hal.laas.fr/hal-03375965/document
https://hal.laas.fr/hal-03375965/document
Publikováno v:
J. Chem. Phys. 154, 224112
We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic forces in m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76143adc222049a4719e352890bdfc13
https://pubmed.ncbi.nlm.nih.gov/34241204
https://pubmed.ncbi.nlm.nih.gov/34241204
Autor:
Franco Pellegrini, Yusuf Shaidu, Ruggero Lot, Stefano de Gironcoli, Efthimios Kaxiras, Emine Kucukbenli
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-13 (2021)
npj Computational Materials
npj Computational Materials
Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96f3c5060143e18531b636eb04702b21
http://arxiv.org/abs/2011.04604
http://arxiv.org/abs/2011.04604