Unsupervised manifold learning on ICSD data reveals a low-dimensional embedding that segregates superconductors and predicts critical temperatures across families.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Graphlet-MP supplies graphlet-histogram representations plus an Earth Mover's Distance metric and open code for 149k crystals, enabling data-efficient structural comparisons without end-to-end DFT training.
citing papers explorer
-
Charting the emergent low-dimensional manifold of quantum materials
Unsupervised manifold learning on ICSD data reveals a low-dimensional embedding that segregates superconductors and predicts critical temperatures across families.
-
Graphlet Histogram Representation Database of Inorganic Crystals
Graphlet-MP supplies graphlet-histogram representations plus an Earth Mover's Distance metric and open code for 149k crystals, enabling data-efficient structural comparisons without end-to-end DFT training.