Uncertainty Prediction for Machine Learning Models of Material Properties

[Image: see text] Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for machine learning (ML) models, p...

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Detalles Bibliográficos
Autores principales: Tavazza, Francesca, DeCost, Brian, Choudhary, Kamal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655759/
https://www.ncbi.nlm.nih.gov/pubmed/34901594
http://dx.doi.org/10.1021/acsomega.1c03752

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655759/
https://www.ncbi.nlm.nih.gov/pubmed/34901594
http://dx.doi.org/10.1021/acsomega.1c03752

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