Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics

• 成果类型: Article
• 署名作者: Fukuda, Jun-ichi; Takahashi, Kazuaki Z.
• 署名单位: Kyushu University; Hiroshima University; National Institute of Advanced Industrial Science & Technology (AIST)
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12024
• DOI: 10.1073/pnas.2412476121
• 发表日期: 2024-12-10
• 关键词: liquid-crystal

摘要

Phase transition between ordered phases has garnered attention from the viewpoint of materials science as well as statistical physics. One interesting example is martensitic transformation and the resulting formation of twin structures, in which atoms or molecules that form one crystalline phase move in a concerted and diffusionless manner toward another crystalline phase. Recently martensitic transformation has been observed experimentally also in various soft materials. However, the complex internal structures involving many molecules have eluded direct investigation of the dynamical processes of martensitic transformation. Here, we carry out a direct type equation for the orientational order parameter with thermal fluctuations. We demonstrate that machine-learning-aided analysis of local structures successfully We further show that twinned BP I is reversibly transformed to a perfect lattice of learning-aided local structure identification provide valuable insights into not only the transitions between ordered phases.