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Can I read A Statistical Learning Protocol to Resolve the Morphological Complexity of Two-dimensional Macromolecules on EtoBox?
A Statistical Learning Protocol to Resolve the Morphological Complexity of Two-dimensional Macromolecules by Yingjie Zhao; Zhiping Xu is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is A Statistical Learning Protocol to Resolve the Morphological Complexity of Two-dimensional Macromolecules about?
## 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).
Who reads A Statistical Learning Protocol to Resolve the Morphological Complexity of Two-dimensional Macromolecules?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Yingjie Zhao; Zhiping Xu
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)