Bauchy, Mathieu

Chen, Zhimin

Du, Tao

Smedskjær, Morten Mattrup

Yue, Yuanzheng

English

    Aalborg UniversitetPredicting Fracture and Conduction Propensity in Glassy Electrolytes UsingClassification-Based Machine LearningDu, Tao; Chen, Zhimin; Bauchy, Mathieu; Yue, Yuanzheng; Smedskjær, Morten MattrupPublication date:2023Link to publication from Aalborg UniversityCitation for published version (APA):Du, T., Chen, Z., Bauchy, M., Yue, Y., & Smedskjær, M. M. (2023). Predicting Fracture and ConductionPropensity in Glassy Electrolytes Using Classification-Based Machine Learning. Abstract from InternationalCommission on Glass Annual Meeting 2023, Hangzhou, China.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            - Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            - You may not further distribute the material or use it for any profit-making activity or commercial gain            - You may freely distribute the URL identifying the publication in the public portal -Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: January 23, 2024Predicting Fracture and Conduction Propensity in Glassy Electrolytes Using Classification-Based Machine Learning Tao Du1*, Zhimin Chen1, Mathieu Bauchy2, Yuanzheng Yue1, Morten M. Smedskjaer1  1 Department of Chemistry and Bioscience, Aalborg University, Aalborg East 9220, Denmark 2 Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, United States *taod@bio.aau.dk  Glasses are promising candidates as solid electrolytes for all-solid-state batteries due to their isotropic ionic conduction, formability, as well as high chemical, thermal and electrochemical stability. However, the lack of well-established composition-structure-property relations hinders the design of new glassy electrolytes with tunable functionalities. Here, we combine molecular dynamics simulations with classification-based machine-learning based to clarify the relation between structure and properties in lithium borophosphate glassy electrolytes. Specifically, we focus on the machine learning derived “softness” metric. By isolating the relative effects of Li and B contents on the electrolyte performance, we correlate the fracture behavior and ionic conduction with the bond switching activities of B and the mobility of Li atoms, respectively. Based on the interpretable features of the machine learning model, we identify the most influential structural parameters of the radial order function controlling the atomic behaviors. These results will help to identify new promising compositions of glassy electrolytes with high mechanical stability and ionic conductivity. 

Predicting Fracture and Conduction Propensity in Glassy Electrolytes Using Classification-Based Machine Learning

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Predicting Fracture and Conduction Propensity in Glassy Electrolytes Using Classification-Based Machine Learning

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