Our group engages in researches about condensed matter theory and material science engineering. Some of our research topics are listed below:
Topological Magnetic materials
The Dancing between topological materials and magnetism will bring new concepts and phenomena to exotic materials, which are key for the new applications in spintronic devices. Based on theoretical tools (DFT and kp theory), our group mainly focus on the following topics.
· AFM topological systems
Antiferromagnetic topological materials have showed great possibilities in not only research of the interplay of Dirac fermion physics and magnetism, but new-generation low-power memory design. We predicted AFM topological fermions in CuMnAs
· Topological Material Engineering
Due to the strong SOC in topological materials, the degrees of freedom of spin and momentum could couple together. Via using the symmetry arguments, we design ways to engineer magnetic and topological properties.
· New Topological Materials
We predicted the new fermions in CoSi and many new topological materials, including 2D quantum spin Hall insulator in dumbbell stanene, the Dirac fermion in hexagonal ABC compound LiZnBi, weak TI in BiTeI-Bi2-BiTeI sandwiched structure and so on. Those topological materials are equipped with many exotic quantum properties and provide material candidates for novel physics and future applications.
Dynamic topological materials
Optical driving is a good way to excite new state of matter and tune the properties of materials in non-equilibrium. Based on the TDDFT and master equations, we explore the transport properties of massless and massive Dirac fermion systems under the photon fields. We argue the non-trivial Hall current have both contributions from optical field driven Berry curvature and charge imbalances.
Machine learning for material sciences
Exciting advances have been made in artificial intelligence (AI) during recent decades. Among them, machine learning and deep learning techniques are widely used in almost all parts of science. Our group engages in applying machine learning to material science, makes full use of neural networks and other machine learning models, and expects to find machines to learn the basic properties of atoms by themselves from the extensive database of known compounds and materials.