MXgap: A MXene Learning Tool for Bandgap Prediction

The increasing demand for clean and renewable energy has intensified the exploration of advanced materials for efficient photocatalysis, particularly for water splitting applications. Among these materials, MXenes, a family of two-dimensional (2D) transition metal carbides and nitrides, have shown g...

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Detalles Bibliográficos
Autores: Ontiveros Cruz, Diego, Vela Llausí, Sergi, Viñes Solana, Francesc, Sousa Romero, Carmen
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Oviedo (UNIOVI)
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/225134
Acceso en línea:https://hdl.handle.net/2445/225134
Access Level:acceso abierto
Palabra clave:MXens
Aprenentatge automàtic
Teoria del funcional de densitat
Fotocatàlisi
MXenes
Machine learning
Density functionals
Photocatalysis
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spelling MXgap: A MXene Learning Tool for Bandgap PredictionOntiveros Cruz, DiegoVela Llausí, SergiViñes Solana, FrancescSousa Romero, CarmenMXensAprenentatge automàticTeoria del funcional de densitatFotocatàlisiMXenesMachine learningDensity functionalsPhotocatalysisThe increasing demand for clean and renewable energy has intensified the exploration of advanced materials for efficient photocatalysis, particularly for water splitting applications. Among these materials, MXenes, a family of two-dimensional (2D) transition metal carbides and nitrides, have shown great promise. This study leverages machine learning (ML) to address the resource-intensive process of predicting the bandgap of MXenes, which is critical for their photocatalytic performance. Using an extensive data set of 4356 MXene structures, we trained multiple ML models and developed a robust classifier-regressor pipeline that achieves a classification accuracy of 92% and a mean absolute error (MAE) of 0.17 eV for bandgap prediction. This framework, implemented in an open-source Python package, MXgap, has been applied to screen 396 La-based MXenes, identifying six promising candidates with suitable band alignments for water splitting whose optical properties were further explored via optical absorption and solar to-hydrogen (STH) efficiency. These findings demonstrate the potential of ML to accelerate MXene discovery and optimization for energy applications.American Chemical Society2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/225134Articles publicats en revistes (Ciència dels Materials i Química Física)reponame:Dipòsit Digital de la UBinstname:Universidad de Oviedo (UNIOVI)InglésReproducció del document publicat a: https://doi.org/10.1021/acscatal.5c04191ACS Catalysis, 2025, vol. 15, p. 14403-14413https://doi.org/10.1021/acscatal.5c04191cc-by (c) Ontiveros Cruz, Diego et al., 2025http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2251342026-05-27T06:46:51Z
dc.title.none.fl_str_mv MXgap: A MXene Learning Tool for Bandgap Prediction
title MXgap: A MXene Learning Tool for Bandgap Prediction
spellingShingle MXgap: A MXene Learning Tool for Bandgap Prediction
Ontiveros Cruz, Diego
MXens
Aprenentatge automàtic
Teoria del funcional de densitat
Fotocatàlisi
MXenes
Machine learning
Density functionals
Photocatalysis
title_short MXgap: A MXene Learning Tool for Bandgap Prediction
title_full MXgap: A MXene Learning Tool for Bandgap Prediction
title_fullStr MXgap: A MXene Learning Tool for Bandgap Prediction
title_full_unstemmed MXgap: A MXene Learning Tool for Bandgap Prediction
title_sort MXgap: A MXene Learning Tool for Bandgap Prediction
dc.creator.none.fl_str_mv Ontiveros Cruz, Diego
Vela Llausí, Sergi
Viñes Solana, Francesc
Sousa Romero, Carmen
author Ontiveros Cruz, Diego
author_facet Ontiveros Cruz, Diego
Vela Llausí, Sergi
Viñes Solana, Francesc
Sousa Romero, Carmen
author_role author
author2 Vela Llausí, Sergi
Viñes Solana, Francesc
Sousa Romero, Carmen
author2_role author
author
author
dc.subject.none.fl_str_mv MXens
Aprenentatge automàtic
Teoria del funcional de densitat
Fotocatàlisi
MXenes
Machine learning
Density functionals
Photocatalysis
topic MXens
Aprenentatge automàtic
Teoria del funcional de densitat
Fotocatàlisi
MXenes
Machine learning
Density functionals
Photocatalysis
description The increasing demand for clean and renewable energy has intensified the exploration of advanced materials for efficient photocatalysis, particularly for water splitting applications. Among these materials, MXenes, a family of two-dimensional (2D) transition metal carbides and nitrides, have shown great promise. This study leverages machine learning (ML) to address the resource-intensive process of predicting the bandgap of MXenes, which is critical for their photocatalytic performance. Using an extensive data set of 4356 MXene structures, we trained multiple ML models and developed a robust classifier-regressor pipeline that achieves a classification accuracy of 92% and a mean absolute error (MAE) of 0.17 eV for bandgap prediction. This framework, implemented in an open-source Python package, MXgap, has been applied to screen 396 La-based MXenes, identifying six promising candidates with suitable band alignments for water splitting whose optical properties were further explored via optical absorption and solar to-hydrogen (STH) efficiency. These findings demonstrate the potential of ML to accelerate MXene discovery and optimization for energy applications.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/225134
url https://hdl.handle.net/2445/225134
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1021/acscatal.5c04191
ACS Catalysis, 2025, vol. 15, p. 14403-14413
https://doi.org/10.1021/acscatal.5c04191
dc.rights.none.fl_str_mv cc-by (c) Ontiveros Cruz, Diego et al., 2025
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Ontiveros Cruz, Diego et al., 2025
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv Articles publicats en revistes (Ciència dels Materials i Química Física)
reponame:Dipòsit Digital de la UB
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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