Machine learning predictions of positron binding to molecules

Use este identificador para citar ou linkar para este item: 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
Resumo:	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.
Assunto:	Elétrons
Idioma:	eng
País:	Brasil
Editor:	Universidade Federal de Minas Gerais
Sigla da Instituição:	UFMG
Departamento:	ICX - DEPARTAMENTO DE FÍSICA
Tipo de Acesso:	Acesso Restrito
Identificador DOI:	https://doi.org/10.1103/PhysRevA.102.052808
URI:	http://hdl.handle.net/1843/64084
Data do 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 nas coleções:	Artigo de Periódico

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