The discovery intrinsic magnetism in monolayer CrI3 and bilayer Cr2 Ge2Te6 created great interest in two-dimensional (2D) materials with intrinsic magnetic order. How many of these materials exist? What are their properties? We present a study of 2D materials with intrinsic magnetic order, materials at the forefront of physics research. We use materials informatics (machine learning applied to materials science) to study the magnetic and thermodynamic properties of 2D materials. Crystal structures based on monolayer Cr2Ge2Te6, of the form A2B2X6, are studied using density functional theory (DFT) calculations and machine learning tools. Magnetic properties, such as the magnetic moment are determined. The formation energies are also calculated and used to estimate the chemical stability. We show that machine learning, combined with DFT, provides a computationally efficient means to predict properties of two-dimensional (2D) magnets. In addition, data analytics provides insights into the microscopic origins of magnetic ordering in 2D. This novel approach to materials research paves the way for the rapid discovery of chemically stable 2D magnetic materials.
Trevor David Rhone received a liberal arts education from Macalester College in Saint Paul. He went on to pursue his doctoral studies at Columbia University in the city of New York where he did experimental studies of two-dimensional electron systems in the extreme quantum limit. Trevor David spent several years at NTT Basic research laboratories in Japan. During a research stint at the National Institute of Materials Science in Japan, he transitioned to materials informatics research - exploiting machine learning tools to perform materials research. He continued this work at Harvard University where he used machine learning tools to search for new 2D magnetic materials.
Trevor David Rhone's research interests involve using machine learning tools for materials discovery and knowledge discovery. Materials discovery could manifest in the search for new 2D materials with exotic properties, the prediction of the outcome of industrially relevant catalytic reactions or for other compelling problems in materials research.