DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures
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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.
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Date issued
2022-03Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics; Massachusetts Institute of Technology. Center for Computational Science and EngineeringJournal
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
ISSN
2159-6867