Speaker
Description
High-Throughput Discovery of Magnetic 2D Materials:
From Physics-Informed Machine Learning to Correlated Electron Systems
The discovery of magnetic two-dimensional (2D) materials has created new opportunities for spintronics, quantum technologies, and low-dimensional correlated electron physics. However, the enormous chemical and structural search space of magnetic monolayers makes conventional first-principles exploration computationally demanding. In this work, a high-throughput framework combining density functional theory (DFT), machine learning (ML), and physics-informed descriptors is presented for accelerated discovery and characterization of magnetic materials.
Transition-metal-based TMXY monolayers are investigated using supervised learning approaches including neural networks, random forest, and gradient boosting models. Key physical properties such as Hubbard (U), lattice parameters, magnetic exchange interactions, magnetic phases, and transition temperatures are predicted. Particular attention is given to descriptor engineering, where chemically and structurally motivated atomic fingerprints are employed to encode local environments, electronic occupation, and superexchange pathways governing magnetism in low-dimensional systems.
Beyond conventional high-throughput screening, recent developments toward integrating experimental and computational datasets within probabilistic data-assimilation frameworks for magnetic materials are discussed. Such approaches enable uncertainty-aware prediction and improved generalization for sparse materials datasets, particularly in rare-earth permanent magnets and correlated magnetic systems. In addition, interpretable machine-learning techniques, including feature-importance and SHAP-based analyses, are utilized to identify physically relevant descriptors controlling magnetic behavior.
Finally, the limitations of standard DFT and purely data-driven approaches in describing finite-temperature magnetism and electronic correlations are discussed, motivating future integration with many-body methods such as DFT+DMFT. The presented framework demonstrates how machine learning, first-principles calculations, experimental feedback, and correlated-electron physics may be combined for next-generation magnetic materials discovery.