Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials
Abstract Biological systems generate a wealth of materials, and their design principles inspire and inform scientists from a broad range of fields. Nature often adapts hierarchical multilevel material architectures to achieve a set of properties for specific functions, providing templat...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer International Publishing
2023
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| Online Access: | https://hdl.handle.net/1721.1/152586 |
| _version_ | 1826193736378351616 |
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| author | Arevalo, Sofia E. Buehler, Markus J. |
| author2 | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics |
| author_facet | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Arevalo, Sofia E. Buehler, Markus J. |
| author_sort | Arevalo, Sofia E. |
| collection | MIT |
| description | Abstract
Biological systems generate a wealth of materials, and their design principles inspire and inform scientists from a broad range of fields. Nature often adapts hierarchical multilevel material architectures to achieve a set of properties for specific functions, providing templates for difficult tasks of understanding the intricate interplay between structure–property–function relationships. While these materials tend to be complex and feature intricate functional interactions across scales, molecular-based multiscale modeling, machine learning, and artificial intelligence combined with experimental approaches to synthesize and characterize materials have emerged as powerful tools for analysis, prediction, and design. This article examines materiomic graph-based modeling frameworks for assisting researchers to pursue materials-focused studies in a biological context, and provides an overview of methods that can be applied to bottom-up manufacturing, including a historical perspective of bioinspired materials research. Through the advent of novel modeling architectures and diverse systems from nature, there is potential to develop materials with improved properties.
Graphical abstract |
| first_indexed | 2024-09-23T09:44:07Z |
| format | Article |
| id | mit-1721.1/152586 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:44:07Z |
| publishDate | 2023 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1525862024-01-22T21:50:12Z Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials Arevalo, Sofia E. Buehler, Markus J. Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Abstract Biological systems generate a wealth of materials, and their design principles inspire and inform scientists from a broad range of fields. Nature often adapts hierarchical multilevel material architectures to achieve a set of properties for specific functions, providing templates for difficult tasks of understanding the intricate interplay between structure–property–function relationships. While these materials tend to be complex and feature intricate functional interactions across scales, molecular-based multiscale modeling, machine learning, and artificial intelligence combined with experimental approaches to synthesize and characterize materials have emerged as powerful tools for analysis, prediction, and design. This article examines materiomic graph-based modeling frameworks for assisting researchers to pursue materials-focused studies in a biological context, and provides an overview of methods that can be applied to bottom-up manufacturing, including a historical perspective of bioinspired materials research. Through the advent of novel modeling architectures and diverse systems from nature, there is potential to develop materials with improved properties. Graphical abstract 2023-10-31T19:49:12Z 2023-10-31T19:49:12Z 2023-10-25 2023-10-29T04:15:45Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/152586 Arevalo, Sofia E. and Buehler, Markus J. 2023. "Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials." PUBLISHER_CC en https://doi.org/10.1557/s43577-023-00610-8 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Arevalo, Sofia E. Buehler, Markus J. Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials |
| title | Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials |
| title_full | Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials |
| title_fullStr | Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials |
| title_full_unstemmed | Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials |
| title_short | Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials |
| title_sort | learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials |
| url | https://hdl.handle.net/1721.1/152586 |
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