Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning
Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning
About this item
Full title
Author / Creator
Liu, Han , Zhang, Tony , Anoop Krishnan, N. M. , Smedskjaer, Morten M. , Ryan, Joseph V. , Gin, Stéṕhane , Bauchy, Mathieu , Energy Frontier Research Centers (EFRC) (United States). Center for Performance and Design of Nuclear Waste Forms and Containers (WastePD) and Alternative Energies and Atomic Energy Commission (CEA), Cadarache (France)
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Machine learning (ML) regression methods are promising tools to develop models predicting the properties of materials by learning from existing databases. However, although ML models are usually good at interpolating data, they often do not offer reliable extrapolations and can violate the laws of physics. Here, to address the limitations of tradit...
Alternative Titles
Full title
Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning
Authors, Artists and Contributors
Author / Creator
Zhang, Tony
Anoop Krishnan, N. M.
Smedskjaer, Morten M.
Ryan, Joseph V.
Gin, Stéṕhane
Bauchy, Mathieu
Energy Frontier Research Centers (EFRC) (United States). Center for Performance and Design of Nuclear Waste Forms and Containers (WastePD)
Alternative Energies and Atomic Energy Commission (CEA), Cadarache (France)
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_osti_scitechconnect_1667366
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_osti_scitechconnect_1667366
Other Identifiers
ISSN
2397-2106
E-ISSN
2397-2106
DOI
10.1038/s41529-019-0094-1
