ABSTRACT
Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach.
ABSTRACT
A major obstacle in the introduction of nanoformulated drugs has been the fact that the shape of the drug delivery systems (DDSs) - the most important parameter driven by the nature of viruses and bacteria - remains almost out-of-scope in artificial systems. Here we propose a potential solution for this problem by developing a template-free approach for the formulation of hollow bacteria-like CaCO3-based pH-sensitive DDSs with controllable anisotropy and click-release behavior.
