Softness Correlations in Low-Temperature Supercooled Liquids

Local structure is known to play a dominant role in determining where structural relaxation occurs [1,2]. This can be quantified using a machine learning approach, yielding a linear model mapping local structure to "softness", a quantity that predicts the propensity of a particle to rearrange [3]. We find that this machine-learned weighted integral of the pair correlation function, when trained on an athermal system relaxing under gradient descent, performs surprisingly well when predicting the dynamics of a supercooled liquid. We use swap Monte Carlo [4] to study the evolution of the spatial correlation of the so defined softness, down to deeply supercooled temperatures. We then compare this length scale to other length scales that have been identified in the literature.

[1] Widmer-Cooper et al., Nature Phys. 4, 711 (2008).
[2] Candelier et al., Phys. Rev. Lett. 105, 135702 (2010).
[3] Cubuk et al., Phys. Rev. Lett. 114, 108001 (2015).
[4] Berthier et al., Nat. Comm. 10, 1508 (2019).