Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Sebastiano PILATI"'
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035015 (2024)
In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state density pro
Externí odkaz:
https://doaj.org/article/d33cfce152d14569b32f085764b6bdc5
Publikováno v:
Molecules, Vol 28, Iss 4, p 1661 (2023)
In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimist
Externí odkaz:
https://doaj.org/article/555bfdce9c09412791542ff39606fa73
Publikováno v:
Condensed Matter, Vol 7, Iss 2, p 30 (2022)
We provide a detailed description of the path-integral Monte Carlo worm algorithm used to exactly calculate the thermodynamics of Bose systems in the canonical ensemble. The algorithm is fully consistent with periodic boundary conditions, which are a
Externí odkaz:
https://doaj.org/article/0cd4a844e480414c84ed7e6a5874b8d6
Publikováno v:
SciPost Physics, Vol 10, Iss 3, p 073 (2021)
We investigate the supervised machine learning of few interacting bosons in optical speckle disorder via artificial neural networks. The learning curve shows an approximately universal power-law scaling for different particle numbers and for diffe
Externí odkaz:
https://doaj.org/article/e06efaaeec1c4535a047d584884323ee
Publikováno v:
Physical Review A. 106
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::099240bec5b18de877ea35da54c6354d
https://doi.org/10.1103/physreva.106.069902
https://doi.org/10.1103/physreva.106.069902
Publikováno v:
Molecules
Volume 28
Issue 4
Pages: 1661
Volume 28
Issue 4
Pages: 1661
In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c64f38f60000198854fa3d30c06e1359
http://arxiv.org/abs/2212.03202
http://arxiv.org/abs/2212.03202
Publikováno v:
Physical review. E. 106(4-2)
Machine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via density functional theory. However, while they can easily be trained
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d599d761df6a88684d6c107e97105040
https://pubmed.ncbi.nlm.nih.gov/36397567
https://pubmed.ncbi.nlm.nih.gov/36397567
Single-component ultracold atomic Fermi gases are usually described using noninteracting many-fermion models. However, recent experiments reached a regime where $p$-wave interactions among identical fermionic atoms are important. In this paper, we em
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e86988981e86160a9461902040ed6e3
Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbdc38870b1df6dd5ae4bc34a43056ce
The ground-state properties of two-component repulsive Fermi gases in two dimensions are investigated by means of fixed-node diffusion Monte Carlo simulations. The energy per particle is determined as a function of the intercomponent interaction stre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::626b8ae2d159e02de3464aceb14fe0ba
https://hdl.handle.net/11581/453540
https://hdl.handle.net/11581/453540