In the first part of this talk, we present the first-principles density functional theory (DFT) modeling of redox properties of various carbon materials such as quinone derivatives, dopamine, corannulene, and coronene. First, we summarize the contribution of electron affinity of molecules and solvation to redox potential from our density functional theory (DFT) calculations. Next, we discuss the relationship of redox potential with the design of molecular structure. By introducing electron-withdrawing and electron-donating functional groups, we can tune up the redox potential precisely. The conclusion is that we can tune up the redox potential by designing the detailed molecular structure, which will be used for machine learning (ML) application. In the second part, we present our machine learning approach towards this topic. Various methods such as artificial neural network, gradient boosting regression, and kernel ridge regression are employed for machine learning to predict the redox potential of carbon materials.  We discuss the validation and predictability of each method. Summarizing, the DFT-ML approach will be useful to design/develop new materials for energy materials including Li-ion battery technology.

Redox potential of carbon materials: First-principles density functional theory modeling and multiple machine learning approaches

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.