SwePub - sökning: WFRF:(Jeschke Steffen 1986)

Numrering	Referens	Omslagsbild	Hitta
1.	Jeschke, Steffen, 1986, et al. (författare) Supervised Machine Learning-Based Classification of Li-S Battery Electrolytes 2021 Ingår i: Batteries and Supercaps. - : Wiley. - 2566-6223. ; 4:7, s. 1156-1162 Tidskriftsartikel (refereegranskat)abstract 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.

Jeschke, Steffen, 1986, et al. (författare)
Supervised Machine Learning-Based Classification of Li-S Battery Electrolytes
2021
Ingår i: Batteries and Supercaps. - : Wiley. - 2566-6223. ; 4:7, s. 1156-1162
Tidskriftsartikel (refereegranskat)abstract
- 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.

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