The discovery of van der Waals (vdW) materials with intrinsic magnetic order in 2017 has given rise to new avenues for the study of emergent phenomena in two dimensions. In particular, monolayer CrI3 was found to be ferromagnet. Other vdW transition metal halides were later found to have different magnetic properties. How many vdW magnetic materials exist in nature? What are their properties? How do these properties change with the number of layers? A conservative estimate for the number of candidate vdW materials (including monolayers, bilayers and trilayers) exceeds ~106. A recent study showed that artificial intelligence (AI) can be harnessed to discover new vdW Heisenberg ferromagnets based on Cr2Ge2Te6 [1,2]. In this talk, we will harness AI to efficiently explore the large chemical space of vdW transition metal halides and to guide the discovery of magnetic vdW materials with desirable spin properties. That is, we investigate crystal structures based on monolayer Cr2I6 of the form A2X6, which are studied using density functional theory (DFT) calculations and AI. Magnetic properties, such as the magnetic moment are determined. The formation energy is also calculated and used as a proxy for the chemical stability. We show that AI, combined with DFT, can provide a computationally efficient means to predict the thermodynamic and magnetic properties of vdW materials . This study paves the way for the rapid discovery of chemically stable magnetic vdW materials with applications in spintronics and data storage.
 T. D. Rhone, et al., “Data-driven Studies of Magnetic Two-dimensional Materials,” Scientific Reports 10, 15795 (2020).
 Y. Xie, et al., “Data-Driven Studies of the Magnetic Anisotropy of Two-Dimensional Magnetic Materials,” J. Phys. Chem. Lett., 12, 50, 12048–12054 (2021).
 T. D. Rhone et al., “Artificial Intelligence Guided Studies of van der Waals Magnets,” Adv. Theory Simulations, 6, 2300019 (2023).
This research was primarily supported by the NSF CAREER, under award number DMR- 2044842.
Trevor David Rhone received a liberal arts education from Macalester College in Saint Paul. He pursued his doctoral studies at Columbia University where he did experimental studies of two- dimensional electron systems in the extreme quantum limit using inelastic light scattering. Rhone spent several years at NTT Basic research laboratories in Japan where he received the BRL director award for his research. While working at the National Institute of Materials Science in Tsukuba, Japan, he transitioned to materials informatics – an emerging field combining materials science with machine learning. He continued this work at Harvard University as a postdoctoral prize fellow where he used machine learning tools to search for new 2D magnetic materials.
Rhone is now a member of the faculty at RPI, where his research interests are at the intersection of materials science and AI. His research goals include the discovery of 2D magnetic materials, in addition to creating physical insight into their behavior. He recently received the NSF CAREER award and the Joseph A. Johnson award for research and mentoring.