Use este identificador para citar o ir al link de este elemento:
http://hdl.handle.net/1843/64084
Tipo: | Artigo de Periódico |
Título: | Machine learning predictions of positron binding to molecules |
Autor(es): | Paulo Henrique Ribeiro Amaral José Rachid Mohallem |
Resumen: | Machine-learning techniques are used to check the theoretical and experimental predictions of positron binding to general molecules. The bound or unbound character of previous calculations for polar molecules are mostly confirmed. Binding for so far unexplored polar molecules is predicted. For apolar molecules, a formula for the binding energy in terms of isotropic polarizability and ionization potential is obtained, leading to unprecedented agreement with experiments as well as prediction of previously unidentified bound systems. The role of the ionization potential is suggested as a consequence of enhanced formation of virtual positronium at short distances. |
Asunto: | Elétrons |
Idioma: | eng |
País: | Brasil |
Editor: | Universidade Federal de Minas Gerais |
Sigla da Institución: | UFMG |
Departamento: | ICX - DEPARTAMENTO DE FÍSICA |
Tipo de acceso: | Acesso Restrito |
Identificador DOI: | https://doi.org/10.1103/PhysRevA.102.052808 |
URI: | http://hdl.handle.net/1843/64084 |
Fecha del documento: | 2020 |
metadata.dc.url.externa: | https://journals.aps.org/pra/abstract/10.1103/PhysRevA.102.052808 |
metadata.dc.relation.ispartof: | Physical Review A |
Aparece en las colecciones: | Artigo de Periódico |
archivos asociados a este elemento:
no existem archivos asociados a este elemento.
Los elementos en el repositorio están protegidos por copyright, con todos los derechos reservados, salvo cuando es indicado lo contrario.