Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Andrea Grisafi"'
Publikováno v:
CHIMIA, Vol 74, Iss 4, Pp 232-236 (2020)
Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in d
Externí odkaz:
https://doaj.org/article/de62de71e2f144d9a3161a2c85747ec2
Autor:
Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti
Publikováno v:
ACS Central Science, Vol 5, Iss 1, Pp 57-64 (2018)
Externí odkaz:
https://doaj.org/article/d28a9cfb45a94c22a91596fd4570b331
Publikováno v:
New Journal of Physics, Vol 21, Iss 10, p 105001 (2019)
Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, espec
Externí odkaz:
https://doaj.org/article/837bda83147441d8a0b7bb3095d39092
Publikováno v:
Computation, Vol 4, Iss 1, p 10 (2016)
In this work, we present a simple decomposition scheme of the Kohn-Sham optimized orbitals which is able to provide a reduced basis set, made of localized polycentric orbitals, specifically designed for Quantum Monte Carlo. The decomposition follows
Externí odkaz:
https://doaj.org/article/c907569721154776a1104b51ab560575
Autor:
Andrea Grisafi, Federico Grasselli
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d699dbc23cf5fd2cbb1c19f458c205f
http://arxiv.org/abs/2212.12233
http://arxiv.org/abs/2212.12233
Publikováno v:
Journal of Chemical Theory and Computation
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
Autor:
Albert P. Bartók, Michele Ceriotti, Félix Musil, Andrea Grisafi, Christoph Ortner, Gábor Csányi
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95dd682636ae7b43bdc2b37339691e2e
Publikováno v:
Chemical Science
Machine learning model of the electron densities for analyzing non-covalent interaction patterns in peptides.
Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. Fr
Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. Fr
Publikováno v:
Journal of Chemical Theory and Computation
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c619a42526ee2f7739a8a328fed8b8a
Publikováno v:
New Journal of Physics
Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, espec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cf219dc9053735c8158c53568cf7d9d
http://arxiv.org/abs/1906.07485
http://arxiv.org/abs/1906.07485