Unraveling the morphological complexity of two-dimensional macromolecules

Folding behaviors of two-dimensional flexible polymers

Unlike one-dimensional polymers, the theoretical framework on the behaviors of two-dimensional (2D) polymers is far from completeness. In this study, we model single-layer flexible 2D polymers of different sizes and examine their scaling behaviors in solution, represented by Rg ∼ Lν, where Rg is the radius of gyration and L is the side length of a 2D polymer. We find that the scaling exponent ν is 0.96 for a good solvent and 0.64 for under poor solvent condition. Interestingly, we observe a previously unnoticed phenomenon: under intermediate solvent conditions, the 2D polymer folds to maintain a flat structure, and as L becomes larger, multiple folded structures emerge. We introduce a shape parameter Q to diagram the relationship of folded structures with the polymer size and solvent condition. Theoretically, we explain the folding transitions by the competition between bending and solvophobic free energies.

A statistical learning protocol to resolve the morphological complexity of two-dimensional macromolecules

Unraveling the morphological complexity of two-dimensional macromolecules allows researchers to design and fabricate high-performance, multifunctional materials. Here, we present a protocol based on statistical learning to resolve morphological complexity utilizing geometrical, topological, and physical features extracted from the strain energy heatmap and structural point cloud. We detail steps for software installation and data generation. We further describe model implementation and evaluation via unsupervised and supervised learning and discuss a theoretical description of morphological complexity including topological features. For complete details on the use and execution of this protocol, please refer to Zhao et al. (2022).

Physical scientist or a data scientist? A journey between disciplines

In a recent publication in Patterns, Zhiping Xu, a group leader at Tsinghua University, China, and his team developed a machine learning approach to unveil the conformational complexities of 2D macromolecules by including the insights of physics and topology. Xu talks about the role of data science in his research and the ongoing process of turning into a data scientist.