Topological Materials, Altermagnetism, and Machine-Learned Interatomic Potentials
This community investigates the electronic and magnetic properties of topological insulators, semimetals, and altermagnetic materials, while simultaneously developing machine-learning-based interatomic potentials to simulate their atomic structures and phase transitions.
The work centers on first-principles calculations and density functional theory to map electronic structures, surface states, and anomalous Hall effects in topological insulators, Weyl semimetals, and Dirac semimetals. A significant portion of the research focuses on altermagnetism, specifically the spin splitting and band structure in materials like CrSb, RuO2, and MnTe. Concurrently, the community develops and applies machine learning techniques, including graph neural networks and deep potential models, to create universal interatomic potentials for large-scale atomistic simulations, molecular dynamics, and the prediction of phase transitions under pressure.
Boron research accounts for the largest share of this community's output, representing 2.9% of all tracked boron research, with 4,115 papers in this group. Bismuth follows with a 2.2% share of its research, and Germanium with a 2.0% share.
The community comprises 43,675 papers, primarily published in Physical Review B, Physical Review Letters, and the Journal of Applied Physics. Recent work continues to focus on the development of universal machine-learning interatomic potentials for advanced materials modeling, the tuning of altermagnetism via strain, and the exploration of metallic p-wave magnets with commensurate spin helices.