Scanning-probe and information-concealing machine learning intermediate hexatic phase and critical scaling of solid-hexatic phase transition in deformable particles

TL;DR

This study uses machine learning to identify and analyze the intermediate hexatic phase and critical scaling in the two-dimensional melting of deformable particles, revealing new insights into phase transition behavior.

Contribution

Introduces two novel machine learning approaches, 'scanning-probe' and 'information-concealing', to detect intermediate phases and analyze critical scaling in complex systems.

Findings

Evidence for the existence of an intermediate hexatic phase.

Critical exponent for the solid-hexatic transition is approximately 0.65.

Clarification of the discontinuous nature of the hexatic-liquid transition.

Abstract

We investigate the two-dimensional melting of deformable polymeric particles with multi-body interactions described by the Voronoi model. We report machine learning evidence for the existence of the intermediate hexatic phase in this system, and extract the critical exponent $\nu\approx0.65$ for the divergence of the correlation length of the associated solid-hexatic phase transition. Moreover, we clarify the discontinuous nature of the hexatic-liquid phase transition in this system. These findings are achieved by directly analyzing system's spatial configurations with two generic machine learning approaches developed in this work, dubbed "scanning-probe" via which the possible existence of intermediate phases can be efficiently detected, and "information-concealing" via which the critical scaling of the correlation length in the vicinity of generic continuous phase transition can be extracted. Our work provides new physical insights into the fundamental nature of the two-dimensional melting of deformable particles, and establishes a new type of generic toolbox to investigate fundamental properties of phase transitions in various complex systems.