Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Machine Learning and Deep Learning in Quantum Materials: Symmetry, Topology, and the Rise of Altermagnets
Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.
- Altermagnetic type-II Multiferroics with N\'{e}el-order-locked Electric Polarization