Zobrazeno 1 - 10
of 897
pro vyhledávání: '"A Grisafi"'
Autor:
Grisafi, Andrea, Salanne, Mathieu
A crucial aspect in the simulation of electrochemical interfaces consists in treating the distribution of electronic charge of electrode materials that are put in contact with an electrolyte solution. Recently, it has been shown how a machine-learnin
Externí odkaz:
http://arxiv.org/abs/2405.07370
Autor:
Palermo, F., Marrocco, N., Dacomo, L., Grisafi, E., Musella, M., Moresi, V., Sanna, A., Massimi, L., Bukreeva, I., Junemann, O., Viola, I., Eckermann, M., Cloetens, P., Weitkamp, T., Gigli, G., Logroscino, G., de Rosbo, N. Kerlero, Balducci, C., Cedola, A.
Alzheimer's disease (AD), a debilitating neurodegenerative disorder, remains one of the foremost public health challenges of our time. Despite decades of research, its etiology largely remains enigmatic. Recently, attention has turned to the gut-brai
Externí odkaz:
http://arxiv.org/abs/2401.14139
The computational study of energy storage and conversion processes calls for simulation techniques that can reproduce the electronic response of metal electrodes under electric fields. Despite recent advancements in machine-learning methods applied t
Externí odkaz:
http://arxiv.org/abs/2304.08966
Autor:
Grisafi, Andrea, Grasselli, Federico
The electrostatic screening properties of ionic fluids are of paramount importance in countless physical processes. Yet, the behavior of ionic conductors out of thermal equilibrium has to date mainly been studied in the context of thermodiffusion phe
Externí odkaz:
http://arxiv.org/abs/2212.12233
The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to represent the sc
Externí odkaz:
http://arxiv.org/abs/2206.14087
Autor:
Schembri, Luca, Caputo, Giuseppe, Ciofalo, Michele, Grisafi, Franco, Lima, Serena, Scargiali, Francesca
Publikováno v:
In Chemical Engineering Science 5 February 2025 302 Part A
Publikováno v:
Journal of Chemical Theory and Computation 2021, 17, 11, 7203-7214
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in a form sui
Externí odkaz:
http://arxiv.org/abs/2106.05364
Autor:
Candiani, Massimo, Dolci, Carolina, Schimberni, Matteo, Bartiromo, Ludovica, Villanacci, Roberta, Grisafi, Giorgia, Tandoi, Iacopo, Salvatore, Stefano, Ferrari, Stefano Maria
Publikováno v:
In European Journal of Obstetrics & Gynecology and Reproductive Biology May 2024 296:163-169
Publikováno v:
In Journal of Water Process Engineering April 2024 60
Publikováno v:
In Scientia Horticulturae 1 March 2024 327