Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors

Simple item page
dc.contributor.authorZhao, Qen_AU
dc.contributor.authorAvdeev, Men_AU
dc.contributor.authorChen, Len_AU
dc.contributor.authorShi, Sen_AU
dc.date.accessioned2021-09-21T23:43:04Zen_AU
dc.date.available2021-09-21T23:43:04Zen_AU
dc.date.issued2021-07-30en_AU
dc.date.statistics2021-09-16en_AU
dc.description.abstractRational design of solid-state electrolytes (SSEs) with high ionic conductivity and low activation energy (Ea) is vital for all solid-state batteries. Machine learning (ML) techniques have recently been successful in predicting Li+ conduction property in SSEs with various descriptors and accelerating the development of SSEs. In this work, we extend the previous efforts and introduce a framework of ML prediction for Ea in SSEs with hierarchically encoding crystal structure-based (HECS) descriptors. Taking cubic Li-argyrodites as an example, an Ea prediction model is developed to the coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.887 and 0.02 eV for training dataset, and 0.820 and 0.02 eV for test dataset, respectively by partial least squares (PLS) analysis, proving the prediction power of HECS-descriptors. The variable importance in projection (VIP) scores demonstrate the combined effects of the global and local Li+ conduction environments, especially the anion size and the resultant structural changes associated with anion site disorder. The developed Ea prediction model directs us to optimize and design new Li-argyrodites with lower Ea, such as Li6–xPS5–xCl1+x (<0.322 eV), Li6+xPS5+xBr1–x (<0.273 eV), Li6+xPS5+xBr0.25I0.75–x (<0.352 eV), Li6+(5–n)yP1–yNyS5I (<0.420 eV), Li6+(5–n)yAs1–yNyS5I (<0.371 eV), Li6+(5–n)yAs1–yNySe5I (<0.450 eV), by broadening bottleneck size, invoking site disorder and activating concerted Li+ conduction. This analysis shows great potential in promoting rational design of advanced SSEs and the same approach can be applied to other types of materials.© 2021 Published by Elsevier B.V. on behalf of Science China Press.en_AU
dc.identifier.citationZhao, Q., Avdeev, M., Chen, L., & Shi, S. (2021). Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors. Science Bulletin, 66(14), 1401-1408. doi:10.1016/j.scib.2021.04.029en_AU
dc.identifier.issn2095-9273en_AU
dc.identifier.issue14en_AU
dc.identifier.journaltitleScience Bulletinen_AU
dc.identifier.pagination1401-1408en_AU
dc.identifier.urihttps://doi.org/10.1016/j.scib.2021.04.029en_AU
dc.identifier.urihttps://apo.ansto.gov.au/dspace/handle/10238/11775en_AU
dc.identifier.volume66en_AU
dc.language.isoenen_AU
dc.publisherElsevieren_AU
dc.subjectCrystal structureen_AU
dc.subjectMachine learningen_AU
dc.subjectElectric batteriesen_AU
dc.subjectLithium ion batteriesen_AU
dc.subjectElectric conductivityen_AU
dc.subjectX-ray diffractionen_AU
dc.titleMachine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptorsen_AU
dc.typeJournal Articleen_AU
Files
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Journal Articles