A search map for organic additives and solvents applicable in high-voltage rechargeable batteries

Abstract

Chemical databases store information such as molecular formulas, chemical structures, and the physical and chemical properties of compounds. Although the massive databases of organic compounds exist, the search of target materials is constrained by a lack of physical and chemical properties necessary for specific applications. With increasing interest in the development of energy storage systems such as high-voltage rechargeable batteries, it is critical to find new electrolytes efficiently. Here we build a search map to screen organic additives and solvents with novel core and functional groups, and thus establish a database of electrolytes to identify the most promising electrolyte for high-voltage rechargeable batteries. This search map is generated from MAssive Molecular Map BUilder (MAMMBU) by combining a high-throughput quantum chemical simulation with an artificial neural network algorithm. MAMMBU is designed for predicting the oxidation and reduction potentials of organic compounds existing in the massive organic compound database, PubChem. We develop a search map composed of ∼1 000 000 redox potentials and elucidate the quantitative relationship between the redox potentials and functional groups. Finally, we screen a quinoxaline compound for an anode additive and apply it to electrolytes and improve the capacity retention from 64.3% to 80.8% near 200 cycles for a lithium ion battery in experiments.