dc.contributor.author | Lederer, Jonas | |
dc.contributor.author | Gastegger, Michael | |
dc.contributor.author | Schütt, Kristof T. | |
dc.contributor.author | Kampffmeyer, Michael Christian | |
dc.contributor.author | Müller, Klaus-Robert | |
dc.contributor.author | Unke, Oliver T. | |
dc.date.accessioned | 2023-12-01T12:35:56Z | |
dc.date.available | 2023-12-01T12:35:56Z | |
dc.date.issued | 2023-08-30 | |
dc.description.abstract | In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of interest are predicted. Here, we introduce a method to automatically identify chemical moieties (molecular building blocks) from such representations, enabling a variety of applications beyond property prediction, which otherwise rely on expert knowledge. The required representation can either be provided by a pretrained MPNN, or be learned from scratch using only structural information. Beyond the data-driven design of molecular fingerprints, the versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases, the automatic construction of coarse-grained force fields, as well as the identification of reaction coordinates. | en_US |
dc.identifier.citation | Lederer, Gastegger, Schütt, Kampffmeyer, Müller, Unke. Automatic identification of chemical moieties. Physical Chemistry, Chemical Physics - PCCP. 2023;25(38):26370-26379 | en_US |
dc.identifier.cristinID | FRIDAID 2185846 | |
dc.identifier.doi | 10.1039/d3cp03845a | |
dc.identifier.issn | 1463-9076 | |
dc.identifier.issn | 1463-9084 | |
dc.identifier.uri | https://hdl.handle.net/10037/31902 | |
dc.language.iso | eng | en_US |
dc.publisher | Royal Society of Chemistry | en_US |
dc.relation.journal | Physical Chemistry, Chemical Physics - PCCP | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2023 The Owner Societies | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0 | en_US |
dc.rights | Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) | en_US |
dc.title | Automatic identification of chemical moieties | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |