Machine learning predictions of positron binding to molecules

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dc.creatorPaulo Henrique Ribeiro Amaral
dc.creatorJosé Rachid Mohallem
dc.date.accessioned2024-02-16T17:35:29Z
dc.date.accessioned2025-09-08T23:30:50Z
dc.date.available2024-02-16T17:35:29Z
dc.date.issued2020
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.1103/PhysRevA.102.052808
dc.identifier.issn2469-9934
dc.identifier.urihttps://hdl.handle.net/1843/64084
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofPhysical Review A
dc.rightsAcesso Restrito
dc.subjectElétrons
dc.subject.otherMachine learning
dc.subject.otherPositron complexes
dc.subject.otherIonization potential
dc.titleMachine learning predictions of positron binding to molecules
dc.typeArtigo de periódico
local.citation.epage052808-5
local.citation.issue5
local.citation.spage052808-1
local.citation.volume102
local.description.resumoMachine-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.
local.identifier.orcidhttps://orcid.org/0000-0003-0799-4143
local.identifier.orcidhttps://orcid.org/0000-0002-4776-4417
local.publisher.countryBrasil
local.publisher.departmentICX - DEPARTAMENTO DE FÍSICA
local.publisher.initialsUFMG
local.url.externahttps://journals.aps.org/pra/abstract/10.1103/PhysRevA.102.052808

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