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.
Supervised and unsupervised learning of the many-body critical phase, phase transitions, and critical exponents in disordered quantum systems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cond-mat.mes-hall 1years
2026 1verdicts
UNVERDICTED 1roles
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.