A tunable microscopic model of network liquids with a liquid-liquid phase transition, analyzed via RFOT theory, predicts nanonucleation near the glass transition and links thermodynamic and kinetic anomalies when matched to water-like conditions.
Mishima , author L
2 Pith papers cite this work. Polarity classification is still indexing.
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fields
cond-mat.soft 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A neural-network temperature classification task plus XAI is used to benchmark 16 structural descriptors for their ability to capture temperature-dependent local order in supercooled water.
citing papers explorer
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Polyamorphism in Glassy Network Materials
A tunable microscopic model of network liquids with a liquid-liquid phase transition, analyzed via RFOT theory, predicts nanonucleation near the glass transition and links thermodynamic and kinetic anomalies when matched to water-like conditions.
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Machine learning evaluation of structural descriptors for supercooled water
A neural-network temperature classification task plus XAI is used to benchmark 16 structural descriptors for their ability to capture temperature-dependent local order in supercooled water.