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

• Published in: Chemical science (Cambridge) Vol. 11; no. 3; pp. 7813 - 7822
• Main Authors: Lee, Byungju, Yoo, Jaekyun, Kang, Kisuk
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 03.07.2020; The Royal Society of Chemistry
• Subjects: Chemical compatibility; Chemical reactions; Chemistry; Electrochemical cells; Electrolytes; Electrolytic cells; Electron affinity; Electronegativity; Functional materials; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Lithium; Machine learning; Mapping; Material properties; Materials selection; Organic materials; Photovoltaic cells; Reactivity; Softness; Solar cells; Stability
• ISSN: 2041-6520; 2041-6539
• DOI: 10.1039/d0sc01328e

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. Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.