DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures
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43579_2022_171_ReferencePDF.pdf
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1.55 MB
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Checksum (MD5)
1820948c9a6b26d503786a7496daa4b0
Author(s)
Buehler, Markus J.
Date Issued
March 2022
Journal
MRS Communications
Publisher
Springer Science and Business Media LLC
Citation
Buehler, Markus J. 2022. "DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures."
Version
Author's final manuscript
Abstract
Abstract
The spontaneous assembly of materials from elementary building blocks is one of the most intriguing natural phenomena. Conventional modeling relies physical approaches to examine such processes. In this paper, a framework is proposed to offer an alternative paradigm, via the use of deep learning, and specifically the use of generative adversarial models as well as a combination of natural language processing and transformer neural nets to create hierarchical assemblies of building blocks. We study the assembly of elementary flame particles into hierarchical materials with features across scales, illustrating the Universality–Diversity Principle (UDP), and create novel material using additive manufacturing.
Graphical abstract
MIT Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1557/s43579-022-00171-y
