Machine learning in condensed matter physics: recent advances and opportunities

With the rapid advances in the machine learning technology in recent years, we are witnessing exploding interests in its applications to solve physics problems. Pioneering work has proven machine learning a promising tool to tackle many frontier research problems in condensed matter physics, such as the identification of phases of matter, solving quantum many-body problems, accelerating materials-by-design, and decreasing computational complexity for simulations, to name a few. Machine learning opens up new research avenues that bridge across theory, experiments and computer simulations, and it enables scientific discoveries that were impossible before. In this talk, I will give a brief overview of the current state-of-the-art, as well as opportunities, in this exciting, emerging area.