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Supervised Machine Learning‐Based Classification of Li−S Battery Electrolytes
Author(s) -
Jeschke Steffen,
Johansson Patrik
Publication year - 2021
Publication title -
batteries and supercaps
Language(s) - English
Resource type - Journals
ISSN - 2566-6223
DOI - 10.1002/batt.202100031
Subject(s) - polysulfide , battery (electricity) , electrolyte , a priori and a posteriori , solubility , computer science , density functional theory , quantitative structure–activity relationship , artificial intelligence , power (physics) , machine learning , thermodynamics , chemistry , computational chemistry , physics , philosophy , electrode , epistemology
Machine learning (ML) approaches have the potential to create a paradigm shift in science, especially for multi‐variable problems at different levels. Modern battery R&D is an area intrinsically dependent on proper understanding of many different molecular level phenomena and processes alongside evaluation of application level performance: energy, power, efficiency, life‐length, etc. One very promising battery technology is Li−S batteries, but the polysulfide solubility in the electrolyte must be managed. Today, many different electrolyte compositions and concepts are evaluated, but often in a more or less trial‐and‐error fashion. Herein, we show how supervised ML can be applied to accurately classify different Li−S battery electrolytes a priori based on predicting polysulfide solubility. The developed framework is a combined density functional theory (DFT) and statistical mechanics (COSMO‐RS) based quantitative structure‐property relationship (QSPR) model which easily can be extended to other battery technologies and electrolyte properties.