dc.contributor.author | Lunghi, Alessandro | |
dc.contributor.author | Nguyen, Vu Ha Anh | |
dc.date.accessioned | 2022-04-21T12:57:59Z | |
dc.date.available | 2022-04-21T12:57:59Z | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022 | en |
dc.identifier.citation | Nguyen, Vu Ha Anh, Lunghi, Alessandro, Predicting tensorial molecular properties with equivariant machine learning models, Physical Review B, 2022, 105, 16 | en |
dc.identifier.issn | 2469-9950 | |
dc.identifier.other | Y | |
dc.description | PUBLISHED | en |
dc.description.abstract | Embedding molecular symmetries into machine-learning models is key for efficient learning of chemico-physical scalar properties, but little evidence on how to extend the same strategy to tensorial quantities exists. Here we formulate a scalable equivariant machine-learning model based on local atomic environment descriptors. We apply it to a series of molecules and show that accurate predictions can be achieved for a comprehensive list of dielectric and magnetic tensorial properties of different ranks.
These results show that equivariant models are a promising platform to extend the scope of machine learning in materials modelling. | en |
dc.language.iso | en | en |
dc.publisher | American Physical Society (APS) | en |
dc.relation.ispartofseries | Physical Review B; | |
dc.relation.ispartofseries | 105; | |
dc.relation.ispartofseries | 16; | |
dc.rights | Y | en |
dc.title | Predicting tensorial molecular properties with equivariant machine learning models | en |
dc.type | Journal Article | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/lunghia | |
dc.identifier.rssinternalid | 242447 | |
dc.identifier.doi | http://dx.doi.org/10.1103/PhysRevB.105.165131 | |
dc.rights.ecaccessrights | openAccess | |
dc.identifier.orcid_id | 0000-0002-1948-4434 | |
dc.identifier.uri | http://hdl.handle.net/2262/98479 | |