First-Principles Modeling Approach Towards Quinone-Derivatives for Li Ion Battery: Effect of Molecular Architecture on Electrochemical Properties

Electrochemical energy storage (EES) systems such as battery and capacitor are important because of their short charge/discharge time, high energy storage efficiency, long cycle life, and ease of integration into renewable energy sources. Currently, lithium-ion batteries have become the dominant power sources for portable electronic devices through the utilization of transition metal oxides or metal phosphates as the positive electrode (Cathode) materials.  Now organic electrode materials have obtained great amount of attention because organic electrode materials are composed of inexpensive and earth abundant elements such as carbon, oxygen, nitrogen, sulfur, and hydrogen, and additionally the structural change of organic materials associated with redox reactions is very small compared to significant volume change of conventional metal and metal oxide materials. More importantly, their properties can be finely tuned by well-established principles of organic chemistry. To develop more cost-effective and sustainable battery technology, organic electrode materials have received lots of attention since organic electrode materials are composed of earth abundant elements such as carbon, oxygen, nitrogen, sulfur, and hydrogen and their properties can be finely tuned via well-established principles of organic chemistry.

In this study, we aim at establishing an integrated design framework to identify high-performance organic electrode materials through the first-principles modeling approach. Thus, various DFT methods are used to obtain the redox potentials of various quinone-derivatives 1) to identify accurate computational method and process; 2) to achieve the structure-property relationship by investigating effect of molecular architectures such as aromaticity and functional groups on electrochemical properties, which can guide the development of new organic materials for battery applications. We compare our computational results with experimental data for validating our approach.