Methods for computing the electronic structure of materials based on quantum mechanics. In our laboratory, we are working with program packages such as VASP and Quantum ESPRESSO.

A research field that applies informatics such as AI and machine learning to materials science. Discovery of new mechanisms related to physical properties and application to new material design are expected.

Methods for predicting a stable crystal structure for a given chemical composition (number of atoms). We are developing highly efficient searching methods using machine learning, and we are also studying its application to new material design.

We are collaborating with other experimental groups and companies to study solid electrolyte materials for lithium-ion batteries by using crystal structure prediction methods and molecular dynamics with machine learning potentials.

We are conducting first-principles electronic state calculations on defect levels in semiconductors. Our research targets compound semiconductors that are experimentally studied in the Tanaka Laboratory at the same course.

We are focusing on the charge, spin, and lattice phase transitions of transition metal oxides with the aim of designing thermal control devices.