Unraveling the morphological complexity of two-dimensional macromolecules
Summary: 2D macromolecules, such as graphene and graphene oxide, possess a rich spectrum of conformational phases. However, their morphological classification has only been discussed by visual inspection, where the physics of deformation and surface contact cannot be resolved. We employ machine lear...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Patterns |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666389922000824 |
| _version_ | 1811258088495251456 |
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| author | Yingjie Zhao Jianshu Qin Shijun Wang Zhiping Xu |
| author_facet | Yingjie Zhao Jianshu Qin Shijun Wang Zhiping Xu |
| author_sort | Yingjie Zhao |
| collection | DOAJ |
| description | Summary: 2D macromolecules, such as graphene and graphene oxide, possess a rich spectrum of conformational phases. However, their morphological classification has only been discussed by visual inspection, where the physics of deformation and surface contact cannot be resolved. We employ machine learning methods to address this problem by exploring samples generated by molecular simulations. Features such as metric changes, curvature, conformational anisotropy and surface contact are extracted. Unsupervised learning classifies the morphologies into the quasi-flat, folded, crumpled phases and interphases using geometrical and topological labels or the principal features of the 2D energy map. The results are fed into subsequent supervised learning for phase characterization. The performance of data-driven models is improved notably by integrating the physics of geometrical deformation and topological contact. The classification and feature extraction characterize the microstructures of their condensed phases and the molecular processes of adsorption and transport, comprehending the processing-microstructures-performance relation in applications. The bigger picture: Resolving morphological complexity of macromolecules is the stepping stone to the design and fabrication of high-performance, multi-functional materials and to understanding the soft matter behaviors in biology and engineering. To extract the physics of lattice distortion and surface contact beyond the conformation is critical, yet challenging. Here, we show that, by labeling the simulation data using the 2D map of potential energies, the 3D geometry, and the topology of contact, morphological classification can be achieved with high accuracy. The well-trained model can be used to decipher the microstructural complexity using simulation or experimental data, which may include the geometrical representation only. This data-driven approach extracts the key geometrical and topological features of 2D macromolecules that are directly responsible for the material performance in relevant applications and can be extended to study other complex surfaces such as red blood cells and the brain. |
| first_indexed | 2024-04-12T18:08:48Z |
| format | Article |
| id | doaj.art-0aba8a85389045a68db1c7d856cdf8d5 |
| institution | Directory Open Access Journal |
| issn | 2666-3899 |
| language | English |
| last_indexed | 2024-04-12T18:08:48Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Patterns |
| spelling | doaj.art-0aba8a85389045a68db1c7d856cdf8d52022-12-22T03:21:54ZengElsevierPatterns2666-38992022-06-0136100497Unraveling the morphological complexity of two-dimensional macromoleculesYingjie Zhao0Jianshu Qin1Shijun Wang2Zhiping Xu3Applied Mechanics Laboratory, Department of Engineering Mechanics and Center for Nano and Micro Mechanics, Tsinghua University, Beijing 100084, ChinaApplied Mechanics Laboratory, Department of Engineering Mechanics and Center for Nano and Micro Mechanics, Tsinghua University, Beijing 100084, ChinaApplied Mechanics Laboratory, Department of Engineering Mechanics and Center for Nano and Micro Mechanics, Tsinghua University, Beijing 100084, ChinaApplied Mechanics Laboratory, Department of Engineering Mechanics and Center for Nano and Micro Mechanics, Tsinghua University, Beijing 100084, China; Corresponding authorSummary: 2D macromolecules, such as graphene and graphene oxide, possess a rich spectrum of conformational phases. However, their morphological classification has only been discussed by visual inspection, where the physics of deformation and surface contact cannot be resolved. We employ machine learning methods to address this problem by exploring samples generated by molecular simulations. Features such as metric changes, curvature, conformational anisotropy and surface contact are extracted. Unsupervised learning classifies the morphologies into the quasi-flat, folded, crumpled phases and interphases using geometrical and topological labels or the principal features of the 2D energy map. The results are fed into subsequent supervised learning for phase characterization. The performance of data-driven models is improved notably by integrating the physics of geometrical deformation and topological contact. The classification and feature extraction characterize the microstructures of their condensed phases and the molecular processes of adsorption and transport, comprehending the processing-microstructures-performance relation in applications. The bigger picture: Resolving morphological complexity of macromolecules is the stepping stone to the design and fabrication of high-performance, multi-functional materials and to understanding the soft matter behaviors in biology and engineering. To extract the physics of lattice distortion and surface contact beyond the conformation is critical, yet challenging. Here, we show that, by labeling the simulation data using the 2D map of potential energies, the 3D geometry, and the topology of contact, morphological classification can be achieved with high accuracy. The well-trained model can be used to decipher the microstructural complexity using simulation or experimental data, which may include the geometrical representation only. This data-driven approach extracts the key geometrical and topological features of 2D macromolecules that are directly responsible for the material performance in relevant applications and can be extended to study other complex surfaces such as red blood cells and the brain.http://www.sciencedirect.com/science/article/pii/S2666389922000824DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem |
| spellingShingle | Yingjie Zhao Jianshu Qin Shijun Wang Zhiping Xu Unraveling the morphological complexity of two-dimensional macromolecules Patterns DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem |
| title | Unraveling the morphological complexity of two-dimensional macromolecules |
| title_full | Unraveling the morphological complexity of two-dimensional macromolecules |
| title_fullStr | Unraveling the morphological complexity of two-dimensional macromolecules |
| title_full_unstemmed | Unraveling the morphological complexity of two-dimensional macromolecules |
| title_short | Unraveling the morphological complexity of two-dimensional macromolecules |
| title_sort | unraveling the morphological complexity of two dimensional macromolecules |
| topic | DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem |
| url | http://www.sciencedirect.com/science/article/pii/S2666389922000824 |
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