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http://hdl.handle.net/1843/64084| Type: | Artigo de Periódico |
| Title: | Machine learning predictions of positron binding to molecules |
| Authors: | Paulo Henrique Ribeiro Amaral José Rachid Mohallem |
| Abstract: | 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. |
| Subject: | Elétrons |
| language: | eng |
| metadata.dc.publisher.country: | Brasil |
| Publisher: | Universidade Federal de Minas Gerais |
| Publisher Initials: | UFMG |
| metadata.dc.publisher.department: | ICX - DEPARTAMENTO DE FÍSICA |
| Rights: | Acesso Restrito |
| metadata.dc.identifier.doi: | https://doi.org/10.1103/PhysRevA.102.052808 |
| URI: | http://hdl.handle.net/1843/64084 |
| Issue Date: | 2020 |
| metadata.dc.url.externa: | https://journals.aps.org/pra/abstract/10.1103/PhysRevA.102.052808 |
| metadata.dc.relation.ispartof: | Physical Review A |
| Appears in Collections: | Artigo de Periódico |
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