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Explore how artificial intelligence can revolutionize the discovery of van der Waals quantum materials in this 49-minute conference talk from Harvard CMSA's Big Data Conference 2025. Learn about the vast chemical space of van der Waals materials, with over 10^6 candidate monolayers and 10^12 potential heterostructures, and discover how AI combined with density functional theory calculations can efficiently navigate this enormous materials landscape. Examine specific case studies involving the discovery of new van der Waals Heisenberg ferromagnets based on Cr2Ge2Te6 and magnetic topological insulators based on MnBi2Te4, while understanding how machine learning approaches can predict thermodynamic and magnetic properties of these quantum materials. Investigate crystal structures of monolayer Cr2I6 compounds (A2X6 form) and MnBi2Te4-based materials (AB2X4 form) to identify novel topological materials with desirable spin and charge properties. Discover how formation energy calculations serve as proxies for chemical stability and explore heterostructures based on MnBi2Te4/Sb2Te3 stacks. Gain insights into the computational methodologies that enable rapid identification of chemically stable van der Waals quantum materials with applications in spintronics, magnetic memory, and quantum computing architectures, supported by extensive research using national computing facilities and NSF funding.
