Exploiting quantum classical crossover to undertake high performance modeling of magnetic materials

Massively parallel implementations of magnetic simulations present huge opportunities in materials discovery and identification. By combining with machine learning, such simulations have the potential to dramatically increase our understanding of spin networks both in terms of novel phase identification as well as revealing new physics. A forefront set of challenges are frustrated magnets and spin liquids. By comparing quantum and classical simulations, it is conjectured that at modest temperatures thermal fluctuations strongly de-phase quantum states and classical simulations become accurate. Here, detailed comparisons are made to many spin networks and quantum-classical crossover is demonstrated experimentally. In addition, the use of such simulations to uncover underlying new physics is shown. The use of advanced simulations and machine learning to accelerate discovery of materials, data analysis, and theoretical understanding are discussed.