Simulating strongly correlated materials by combining quantum theory with machine learning

Answers to fundamental scientific questions inherent in the so-called “strongly correlated” materials (e.g., the high-temperature superconductors) are still beyond our reach. The main obstacle for solving these problems is that, with presently available theoretical tools, computations are often too slow to be ever feasible. Here we propose an unconventional and ground-breaking method combining quantum theory with Machine Learning (ML) to solve this problem, which may change drastically the way how we approach quantum simulations today, paving the way to virtually infinite applications in condensed matter physics, chemistry and materials science.