Representation of molecular structures with persistent homology for machine learning applications in chemistry

The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO2/N2 interaction energies at the density functional theory...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jacob Townsend, Cassie Putman Micucci, John H. Hymel, Vasileios Maroulas, Konstantinos D. Vogiatzis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/5a638e6c37334f2eb4cc47e69d8c65d8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5a638e6c37334f2eb4cc47e69d8c65d8
record_format	dspace
spelling	oai:doaj.org-article:5a638e6c37334f2eb4cc47e69d8c65d82021-12-02T17:45:10ZRepresentation of molecular structures with persistent homology for machine learning applications in chemistry10.1038/s41467-020-17035-52041-1723https://doaj.org/article/5a638e6c37334f2eb4cc47e69d8c65d82020-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17035-5https://doaj.org/toc/2041-1723The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO2/N2 interaction energies at the density functional theory (DFT) level.Jacob TownsendCassie Putman MicucciJohn H. HymelVasileios MaroulasKonstantinos D. VogiatzisNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jacob Townsend Cassie Putman Micucci John H. Hymel Vasileios Maroulas Konstantinos D. Vogiatzis Representation of molecular structures with persistent homology for machine learning applications in chemistry
description	The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO2/N2 interaction energies at the density functional theory (DFT) level.
format	article
author	Jacob Townsend Cassie Putman Micucci John H. Hymel Vasileios Maroulas Konstantinos D. Vogiatzis
author_facet	Jacob Townsend Cassie Putman Micucci John H. Hymel Vasileios Maroulas Konstantinos D. Vogiatzis
author_sort	Jacob Townsend
title	Representation of molecular structures with persistent homology for machine learning applications in chemistry
title_short	Representation of molecular structures with persistent homology for machine learning applications in chemistry
title_full	Representation of molecular structures with persistent homology for machine learning applications in chemistry
title_fullStr	Representation of molecular structures with persistent homology for machine learning applications in chemistry
title_full_unstemmed	Representation of molecular structures with persistent homology for machine learning applications in chemistry
title_sort	representation of molecular structures with persistent homology for machine learning applications in chemistry
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/5a638e6c37334f2eb4cc47e69d8c65d8
work_keys_str_mv	AT jacobtownsend representationofmolecularstructureswithpersistenthomologyformachinelearningapplicationsinchemistry AT cassieputmanmicucci representationofmolecularstructureswithpersistenthomologyformachinelearningapplicationsinchemistry AT johnhhymel representationofmolecularstructureswithpersistenthomologyformachinelearningapplicationsinchemistry AT vasileiosmaroulas representationofmolecularstructureswithpersistenthomologyformachinelearningapplicationsinchemistry AT konstantinosdvogiatzis representationofmolecularstructureswithpersistenthomologyformachinelearningapplicationsinchemistry
_version_	1718379588143284224

Representation of molecular structures with persistent homology for machine learning applications in chemistry

Ejemplares similares