AI Identification of the Intertwined Electronic Ordered State Hidden in Complex Electronic Structure Images

With artificial intelligence(AI) revolutionizing data science across disciplines, it is natural to ask whether AI can help us improve the understanding of quantum electronic matter. However, quantum mechanical imaging of electronic behavior, for instance using scanning tunneling microscopy(STM), is probabilistic and hence its interpretation is highly non-trivial. Guided by recent success in training AI to recognize key defining features of many-body states from simulation data, we introduce a general protocol for revealing driving forces of emergent intertwined order in experimental data for quantum matter through an AI-human coalition. Following this protocol, we build and train artificial neural networks to differentiate simulated STM data associated with different theoretical hypotheses. We then employ a group of trained networks to test experimental data obtained from high T_c cuprates over a range of doping. Remarkably the AIs report a feature in the patterns of symmetry breaking in the STM data that points to a universal real-space based mechanism. Hence, we establish that the proposed AI-human coalition can drive future discoveries of quantum phenomena.