Geometric memory in incomplete phase transitions across dimensions

TL;DR

This study models geometric memory effects in phase transitions across different dimensions, revealing that memory strength varies with geometry and dimensionality, and introduces a quantitative descriptor for this phenomenon.

Contribution

It extends a 2D phase transition model to 3D and lamellar geometries, analyzing how dimensionality influences geometric memory in solid-solid transformations.

Findings

Memory is stronger in 2D than in 3D or 3DL geometries.

The size mass ratio effectively quantifies the memory effect.

Geometric blocking and dimensionality control transformation memory strength.

Abstract

We model a direct solid-state phase transition through a nucleation-and-growth process in which plates have simple, regular shapes - squares, cubes, or square-faced lamellae - and grow homothetically (self-similarly) until they either reach a randomly assigned maximum size or are stopped by impingement with previously formed plates. The reverse transformation is represented by the preferential disappearance of smaller plates, while larger plates are retained during an incomplete reversion. A subsequent direct transformation therefore produces a modified plate-size distribution, a memory effect that forms the main focus of this study. Building upon an earlier two-dimensional (2D) formulation, we extend the model to cubes (3D) and to lamellar plates (3DL) in order to examine how dimensionality affects transformation memory. We introduce a quantitative descriptor of memory, the size mass ratio, and find that memory is robust in all geometries but overall stronger in 2D than in 3D or 3DL. We provide growth snapshots, arrest-regrowth cycles, size distributions, and differential scanning calorimetry simulations, and we compute the Shannon size-entropy to quantify configurational diversity. Although motivated by the thermal memory effect in shape-memory alloys, the model more generally identifies a purely geometric mechanism for memory in first-order solid-solid transformations, highlighting the role of dimensionality and geometric blocking in controlling the strength of transformation memory.