Mandal, Indrajeet and Mannan, Sajid and Wondraczek, Lothar and Gosvami, Nitya Nand and Allu, Amarnath R R and Krishnan, N M Anoop (2023) Machine Learning-Assisted Design of Na-Ion-Conducting Glasses. Journal of Physical Chemistry C, 127 (30). pp. 14636-14644. ISSN 1932-7447

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Abstract

Asan alternative to liquid electrolytes, all-solid-state sodium-ionbatteries are receiving significant attention due to their potentialfor improved safety and efficiency. Here, we propose a combined experimentaland machine learning (ML) approach for discovering glass electrolyteswhile also providing insights into the role of different glass components.Specifically, we experimentally prepare and measure the ionic conductivityof 27 glass compositions of the sodium aluminophosphate glass family.Further, we train ML models on this dataset to predict the ionic conductivity,which exhibits excellent agreement with the experimental results.We interpret the composition-conductivity relationship learnedby the ML model using Shapely additive explanations (SHAP), whichreveals the role played by the glass components in governing the conductivity.Employing these observations, glass compositions with improved conductivityvalues are predicted and experimentally validated. The results corroboratethe insights from SHAP analysis and enable optimized glass formulationsin real-world experiments. This demonstrates how ML tools can significantlyaccelerate the discovery of Na-ion-conducting glass electrolytes.

Item Type:	Article
Subjects:	Glass
Divisions:	Glass
Depositing User:	Bidhan Chaudhuri
Date Deposited:	13 Oct 2023 09:06
Last Modified:	13 Oct 2023 09:06
URI:	http://cgcri.csircentral.net/id/eprint/5625

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