High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms

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dc.contributor.authorHe, Ben_AU
dc.contributor.authorChi, STen_AU
dc.contributor.authorYe, AJen_AU
dc.contributor.authorMi, PHen_AU
dc.contributor.authorZhang, LWen_AU
dc.contributor.authorPu, Ben_AU
dc.contributor.authorZou, Zen_AU
dc.contributor.authorRan, YBen_AU
dc.contributor.authorZhao, Qen_AU
dc.contributor.authorWang, Den_AU
dc.contributor.authorZhang, WQen_AU
dc.contributor.authorZhao, JTen_AU
dc.contributor.authorAdams, Sen_AU
dc.contributor.authorAvdeev, Men_AU
dc.contributor.authorShi, Sen_AU
dc.date.accessioned2021-06-29T21:11:29Zen_AU
dc.date.available2021-06-29T21:11:29Zen_AU
dc.date.issued2020-05-21en_AU
dc.date.statistics2021-06-28en_AU
dc.description.abstractThe combination of a materials database with high-throughput ion-transport calculations is an effective approach to screen for promising solid electrolytes. However, automating the complicated preprocessing involved in currently widely used ion-transport characterization algorithms, such as the first-principles nudged elastic band (FP-NEB) method, remains challenging. Here, we report on high-throughput screening platform for solid electrolytes (SPSE) that integrates a materials database with hierarchical ion-transport calculations realized by implementing empirical algorithms to assist in FP-NEB completing automatic calculation. We first preliminarily screen candidates and determine the approximate ion-transport paths using empirical both geometric analysis and the bond valence site energy method. A chain of images are then automatically generated along these paths for accurate FP-NEB calculation. In addition, an open web interface is actualized to enable access to the SPSE database, thereby facilitating machine learning. This interactive platform provides a workflow toward high-throughput screening for future discovery and design of promising solid electrolytes and the SPSE database is based on the FAIR principles for the benefit of the broad research community. © 2020, The Author(s)en_AU
dc.identifier.articlenumber151en_AU
dc.identifier.citationHe, B., Chi, S., Ye, A., Mi, P., Zhang, L., Pu, B., Zou, Z., Ran, Y., Zhao, Q., Wang, D., Zhang, W., Zhao, J., Adams, S., Avdeev, M., & Shi, S. (2020). High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms. Scientific Data, 7, 1-14, 151. doi:10.1038/s41597-020-0474-yen_AU
dc.identifier.issn2052-4463en_AU
dc.identifier.journaltitleScientific Dataen_AU
dc.identifier.pagination1-14en_AU
dc.identifier.urihttps://doi.org/10.1038/s41597-020-0474-yen_AU
dc.identifier.urihttps://apo.ansto.gov.au/dspace/handle/10238/10954en_AU
dc.identifier.volume7en_AU
dc.language.isoenen_AU
dc.publisherSpringer Natureen_AU
dc.subjectRadioisotope batteriesen_AU
dc.subjectElectrolytesen_AU
dc.subjectComputerized simulationen_AU
dc.subjectSolid electrolytesen_AU
dc.subjectCrystal structureen_AU
dc.subjectMonte Carlo Methoden_AU
dc.titleHigh-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithmsen_AU
dc.typeJournal Articleen_AU
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