A statistical learning protocol to resolve the morphological complexity of two-dimensional macromolecules
Summary: 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,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166722006475 |
| Summary: | Summary: 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). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
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| ISSN: | 2666-1667 |