Correlative image learning of chemo-mechanics in phase-transforming solids.
Journal
Nature materials
ISSN: 1476-4660
Titre abrégé: Nat Mater
Pays: England
ID NLM: 101155473
Informations de publication
Date de publication:
May 2022
May 2022
Historique:
received:
12
07
2021
accepted:
16
12
2021
pubmed:
19
2
2022
medline:
19
2
2022
entrez:
18
2
2022
Statut:
ppublish
Résumé
Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (for example, due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. Here we developed a generalizable, physically constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on Li
Identifiants
pubmed: 35177785
doi: 10.1038/s41563-021-01191-0
pii: 10.1038/s41563-021-01191-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
547-554Subventions
Organisme : DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab)
ID : DE-AC02-05CH11231
Organisme : DOE | SC | Basic Energy Sciences (BES)
ID : DE-SC0010412
Organisme : DOE | SC | Basic Energy Sciences (BES)
ID : DE-AC02-05CH11231
Organisme : DOE | SC | Basic Energy Sciences (BES)
ID : DE-AC02-76SF00515
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.
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