Computational Materials Science for Electronic, Magnetic and Thermal Properties
This community uses first-principles calculations and machine learning to predict the electronic, magnetic, and thermal behavior of inorganic solids, including semiconductors, superconductors, and topological materials.
The work centers on density functional theory and molecular dynamics simulations to model crystal structures, phase transitions, and band gaps. Recurring subjects include boron clusters, silicon carbide, rare-earth compounds, and transition metal oxides. Researchers investigate anomalous Hall effects, superconductivity, and thermal expansion in thin films and two-dimensional materials. Machine learning potentials and graph neural networks are increasingly used to accelerate these simulations and explore chemical space for new material candidates.
The largest share of the community's output is found in Germanium research, accounting for 4.1% of all Germanium research, and 1,114 papers here. Boron research also represents a significant portion, with 3.5% of its total output and 1,189 papers in this group.
The community comprises 18,098 papers, primarily published in Physical Review B, Journal of Applied Physics, and The Journal of Physical Chemistry C.
Recent work focuses on developing universal interatomic potentials for advanced materials modeling and applying machine learning to heterogeneous catalysis and inverse design of functional materials.
Papers behind this description
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules — Journal of the American Chemical Society, 2025 — doi:10.1021/jacs.4c07099
- An anomalous Hall effect in altermagnetic ruthenium dioxide — Nature Electronics, 2022 — doi:10.1038/s41928-022-00866-z
- Large-scale chemical language representations capture molecular structure and properties — Nature Machine Intelligence, 2022 — doi:10.1038/s42256-022-00580-7
- Crystal structure generation with autoregressive large language modeling — Nature Communications, 2024 — doi:10.1038/s41467-024-54639-7
- Systematic softening in universal machine learning interatomic potentials — npj Computational Materials, 2025 — doi:10.1038/s41524-024-01500-6
- General reactive element-based machine learning potentials for heterogeneous catalysis — Nature Catalysis, 2025 — doi:10.1038/s41929-025-01398-3
- Zero thermal expansion and magnetocaloric effect in B doped Fe2(Hf,Ta) Laves phase compounds — Acta Materialia, 2025 — doi:10.1016/j.actamat.2025.121687
- All-electrically controlled spintronics in altermagnetic heterostructures — npj Quantum Materials, 2025 — doi:10.1038/s41535-025-00827-7