Learning optimal forms of constitutive relations characterizing ion intercalation from data in mathematical models of lithium-ion batteries

Lindsey Daniel (Creator)
Smita Sahu (Creator)
Kevin J. Sanders (Creator)
Gillian R. Goward (Creator)
Jamie Foster (Creator)
Bartosz Protas (Creator)

School of Mathematics & Physics

Dataset

Description

Supporting Information

Date made available	30 Sept 0007
Publisher	The Journal of Physical Chemistry C
Date of data production	7 Sept 2023

DOI
10.1021/acs.jpcc.3c02915

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jp3c02915_si_001.pdf
File: application/pdf, 211 KB
Type: Dataset
Licence: CC BY
Embargo End Date: 7 Sept 2023

Supporting Information

Learning optimal forms of constitutive relations characterizing ion intercalation from data in mathematical models of lithium-ion batteries
Daniels, L., Sahu, S., Sanders, K. J., Goward, G. R., Foster, J. M. & Protas, B., 7 Sept 2023, In: The Journal of Physical Chemistry C. 127, 35, p. 17508–17523 16 p., 35.
Research output: Contribution to journal › Article › peer-review

Open Access
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DataSetCite

Daniel, L. (Creator), Sahu, S. (Creator), Sanders, K. J. (Creator), Goward, G. R. (Creator), Foster, J. (Creator), Protas, B. (Creator) (30 Sept 0007). Learning optimal forms of constitutive relations characterizing ion intercalation from data in mathematical models of lithium-ion batteries. The Journal of Physical Chemistry C. jp3c02915_si_001(.pdf). 10.1021/acs.jpcc.3c02915

Learning optimal forms of constitutive relations characterizing ion intercalation from data in mathematical models of lithium-ion batteries

Description

DOI

Access Dataset

Research output

Learning optimal forms of constitutive relations characterizing ion intercalation from data in mathematical models of lithium-ion batteries

Cite this