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dc.contributor.author
Miccio, Luis Alejandro
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dc.contributor.author
Borredon, Claudia
dc.contributor.author
Schwartz, Gustavo A.
dc.date.available
2024-08-30T12:58:04Z
dc.date.issued
2024-03
dc.identifier.citation
Miccio, Luis Alejandro; Borredon, Claudia; Schwartz, Gustavo A.; A glimpse inside materials: Polymer structure – Glass transition temperature relationship as observed by a trained artificial intelligence; Elsevier Science; Computational Materials Science; 236; 3-2024; 1-7
dc.identifier.issn
0927-0256
dc.identifier.uri
http://hdl.handle.net/11336/243340
dc.description.abstract
Artificial neural networks (ANNs), a subset of Quantitative Structure-Property Relationship (QSPR) methods, offer a promising avenue for addressing challenges in materials science. In particular, ANNs can learn intricated patterns within the experimental data, enabling them to predict properties and recognize complex relationships with remarkable accuracy. However, the opacity of ANNs, normally acting as black boxes, raises concerns about their reliability and interpretability. To enhance their transparency and to uncover the underlying relationships between chemical features and material properties, we propose a novel approach that employs Gradient-weighted Class Activation Mapping (Grad-CAM) applied to Convolutional Neural Networks (CNNs). By analyzing these attention maps, we identify the crucial chemical features influencing the prediction of a polymer property, specifically the glass transition temperature (Tg). Our methodology is validated using a dataset of atactic acrylates, allowing us to not only predict Tg values for a control group of polymers but also to quantitatively assess the impact of individual monomer structural elements on these predictions. This work proposes a step towards transparent models in materials science, contributing to a deeper understanding of the intricate relationship between chemical structures and material properties.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Artificial Neural networks
dc.subject
Gradient-weighted Class Activation Mapping
dc.subject
Convolutional Neural Networks (CNNs)
dc.subject
glass transition temperature (Tg)
dc.subject.classification
Ingeniería de los Materiales
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dc.subject.classification
Ingeniería de los Materiales
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
A glimpse inside materials: Polymer structure – Glass transition temperature relationship as observed by a trained artificial intelligence
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-08-26T11:00:25Z
dc.journal.volume
236
dc.journal.pagination
1-7
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Miccio, Luis Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina. Consejo Superior de Investigaciones Científicas; España
dc.description.fil
Fil: Borredon, Claudia. Consejo Superior de Investigaciones Científicas; España
dc.description.fil
Fil: Schwartz, Gustavo A.. Consejo Superior de Investigaciones Científicas; España
dc.journal.title
Computational Materials Science
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.commatsci.2024.112863
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0927025624000843
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