(537a) First-Principles Density Functional Theory Modeling and Machine Learning Approach for Redox Potential of Carbon Materials

In this talk, we present the first-principles density functional theory modeling of redox properties of various carbon materials, including quinone derivatives, dopamine, and DNA. First, we discuss the contribution of electron affinity of molecules and solvation to redox potential from our density functional theory (DFT) calculations. Second, we discuss the relationship of redox potential with the design of molecular structure. It is found that aromaticity and electron-withdrawing functional groups can be used to tune the redox potential. Especially, the variation of redox potential as a function of Li association is discussed. The conclusion is that we can tune up the redox potential by designing the detailed molecular structure. Next, all these results are used to build up machine learning. We discuss our artificial neural network and other machine learning algorithms regarding its validation and predictability. Through this study, the redox characteristics of carbon materials are systematically investigated using the first-principles computational modeling method, and then utilized via machine learning methods for high-throughput screening process.