Machine Learning Surrogates for Simulating Quantum Materials

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Quantum materials have unusual magnetic and electrical properties that, if understood and controlled, will revolutionize the technology sector, e.g. enable highly energy-efficient electrical systems or faster electronic devices.This project contributes to the development of computationally efficient and robust "virtual laboratories" in which quantum materials systems can be studied far more cheaply and in a more targeted way than in experiments. This will enable high through-put screening of candidate quantum systems and thus accelerate scientific discovery and technology transfer. The barrier to this goal is that, despite the continuing rapid increase in computational resources available to researchers, high-fidelity simulation of quantum materials systems of scientific and technological interest remains out of reach. The critical factors are the scale of such quantum systems and the need to accurately treat strong correlation effects to describe the emergence of genuine quantum phenomena. The objective of this Alliance-Quantum-Catalyst project is to significantly expand the range of quantum systems that can be simulated reliably and accurately by merging modern machine-learning methodology with deep insights into the chemistry and physics of quantum materials systems.