ArticleProceedings of the National Academy of Sciences of the United States of America2026
Clever algorithms for glasses work by time reparameterization.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Synergistic coupling of structure, dynamics and electronics during the glass transition of rapidly quenched LiRSC advances · 2026Article
- Beyond geometry orders: uncovering bonding-heterogeneity-dominated structure-relaxation coupling in glasses.National science review · 2026Article
- Clever algorithms for glasses work by time reparameterization.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
The ultraslow dynamics of glass-formers has been explained by two views often considered as mutually exclusive: One invokes locally hindered mobility, and the other rests on the complexity of the configuration space. Here, we show that time evolution responds strongly to details of the dynamics by changing the speed of time flow: It has time-reparameterization softness. This finding reconciles both views: While local constraints reparameterize the flow of time, the global landscape determines relationships between different correlations at the same times. We show that modern algorithms developed to accelerate the relaxation to equilibrium act by changing the time reparameterization. Their success thus relies on their ability to exploit reparameterization softness. We conjecture that these results extend beyond the realm of glasses to the optimization of more general constraint satisfaction problems and to broader classes of algorithms.
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