Computational efficiency and scalability are also critical for efficient inverse design based on first-principles calculations. To enable simulations of large material systems (thousands of atoms or more) within seconds, our team focuses on integrating machine learning techniques into modern first-principles software (e.g. FHI-aims, Abinit, QE, VASP, etc.). This approach aims to achieve DFT-level accuracy at significantly reduced computational cost. This is also a new area in our group, we are welcoming ambitious students who want to join this project and change the future of the first principle calculations together.