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

Chemical Science
Byungju LeeKisuk Kang

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 lar...Continue Reading

References

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Citations

Sep 23, 2021·Journal of Chemical Information and Modeling·Samuel BoobierBao N Nguyen
Nov 19, 2021·Accounts of Chemical Research·Giyun KwonKisuk Kang

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Software Mentioned

RDKit
Adam
Gaussian

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