Materials or systems used in secondary batteries are being studied more broadly, in depth, and efficiently using computational chemistry or AI. Computational chemistry based on quantum mechanics helps us understand the behavior of materials used in secondary batteries by calculating and interpreting what happens at the atomic level. For example, it predicts the charge/discharge rate of a battery by predicting the movement of lithium atoms, which is difficult to measure experimentally, and suggests how the atomic structure of the active material used is changed.
Recent secondary batteries incorporate AI (artificial intelligence) to design secondary batteries faster and more effectively. Through self-learning, it is showing remarkable progress in proposing a material design that is more stable and having a higher energy density and predicting the lifespan of a battery. As such, secondary battery research using computational chemistry or AI is an essential strategy for a better future, and our graduate school is researching active materials/functional electrode materials/next-generation secondary battery technology using computational chemistry and AI.