BWhat changed
Predicts particle mobility and rearrangement in simulated glassy systems from graph-encoded initial structure, and probes spatial correlations learned by the network.
Scientific scope
Predicts particle mobility and rearrangement in simulated glassy systems from graph-encoded initial structure, and probes spatial correlations learned by the network. The source-supported claim concerns learned structural representations and prediction of particle dynamics. A learned correlation length is not directly a measured physical quantity or a solution of glass-transition theory. The later code/data-link correction is not a new discovery.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The source-supported claim concerns learned structural representations and prediction of particle dynamics. A learned correlation length is not directly a measured physical quantity or a solution of glass-transition theory. The later code/data-link correction is not a new discovery.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- Unveiling the predictive power of static structure in glassy systems
- Towards understanding glasses with graph neural networks
- Author Correction: Unveiling the predictive power of static structure in glassy systems
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
Full scientific record · Browse contribution grades · Request a correction