Machine-learning phase transition paper published in Newton

Our collaborative paper with Dr. Liu and Dr. He, which uses domain-adversarial machine learning to identify fluctuating and long-range ordered superconductivity from ARPES spectrum, has been published in Newton! Our story is also selected as the cover! Congrats to Xu and Yuanjie, who led the development of method. This work solved the long-standing scarcity of experimental data in AI for Science problems, by combining experimental and theoretical spectra in a domain adversarial manner.