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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cond-mat.soft 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ML-potential MD simulations of sodium disilicate, tetrasilicate and hexasilicate melts show sodium hopping via bimodal van Hove functions and strongest non-Gaussian parameter for oxygen atoms.
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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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Dynamic heterogeneity in sodium silicate melts via machine-learning potential
ML-potential MD simulations of sodium disilicate, tetrasilicate and hexasilicate melts show sodium hopping via bimodal van Hove functions and strongest non-Gaussian parameter for oxygen atoms.