Researchers investigate how cubic liquid crystals change between different phases through direct simulation and machine learning, paving the way for enhanced materials development.
A team of researchers has made significant discoveries regarding the phase transitions of liquid crystals, which are materials that can form intricate ordered structures. Their findings, published in PNAS, clarify the mechanisms at play during these microscopic structural changes. This research may provide a more profound understanding of structural transformations in a broader range of materials.
Liquid crystals uniquely combine the characteristics of liquids and solids. They can flow like liquids while also organizing into solid-like structures. These materials are widely implemented in technologies such as digital screens, light-sensitive materials, and sensors. Despite their common applications, the microscopic reorganization processes of liquid crystals have posed a long-standing challenge for scientists, with many mechanisms remaining elusive.
Professor Jun-ichi Fukuda from Kyushu University’s Department of Physics, alongside Dr. Kazuaki Z. Takahashi from the National Institute of Advanced Industrial Science and Technology (AIST) and the Japan Science and Technology Agency (JST), conducted a focused study on cholesteric blue phases. These specific liquid crystals are notable for their distinct cubic symmetry and can form intricate three-dimensional structures that hold substantial interest for both fundamental science and materials engineering.
The researchers explored the transition from one blue phase known as BP II to another known as BP I. During this change, the liquid crystal generates twin boundaries, areas where two segments of the material align differently. Past experimental investigations have not been able to fully elucidate the mechanisms behind the transformations of blue phases that lead to twin structure formation.
To deepen their understanding, the team utilized computer simulations crafted by Fukuda, along with MALIO, a machine learning tool created by Takahashi, designed to analyze and differentiate between the local structures of BP I and BP II liquid crystal phases. This machine learning application enabled the researchers to distinguish between the two phases and to track their transformation over time. Their innovative strategy allowed real-time monitoring of the changes, unveiling crucial stages in the transition, including the development of small BP I regions that grow and ultimately create twin boundaries. This approach offers valuable insights into how twin structures form and expand during the transition.
“The dynamics of soft materials like liquid crystals are incredibly intricate,” states Fukuda. “This research has enriched our understanding of their transformations at a microscopic scale.”
The methodology introduced in this study may also illuminate how hierarchical structures in soft materials, such as polymers and biological systems, experience similar phase changes. “Our approach is not confined to liquid crystals,” Fukuda clarifies. “It is applicable to other complex materials, potentially yielding new insights into how structures evolve and modify within various systems.”
