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Predicting the chemical reactivity of organic materials using a machine-learning approach

Cited 1 time in Web of Science Cited 2 time in Scopus
Authors
Lee, Byungju; Yoo, Jaekyun; Kang, Kisuk
Issue Date
2020-08
Citation
Chemical Science, Vol.11 No.30, pp.7813-7822
Abstract
Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database,i.e.data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium-oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium-oxygen batteries.
ISSN
2041-6520
URI
https://hdl.handle.net/10371/171796
DOI
https://doi.org/10.1039/d0sc01328e
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Material Science and Engineering (재료공학부) Journal Papers (저널논문_재료공학부)
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