Expanding Materials Selection Via Transfer Learning for High-Temperature Oxide Selection

Materials with higher operating temperatures than today’s state of the art can improve system performance in several applications and enable new technologies. Under most scenarios, a protective oxide scale with high melting temperatures and thermodynamic stability as well as low ionic diffusivity is...

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Veröffentlicht in:	JOM (1989) Jg. 73; H. 1; S. 103 - 115
Hauptverfasser:	McClure, Zachary D., Strachan, Alejandro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2021 Springer Nature B.V
Schlagworte:	Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification Chemistry/Food Science Data science Datasets Earth Sciences Elastic properties Engineering Environment First principles High temperature Learning Machine learning Materials selection Melt temperature Melting New technology Operating temperature Oxidation Oxides Physics Prediction models Scale (corrosion) Science Simulation
ISSN:	1047-4838, 1543-1851
Online-Zugang:	Volltext
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