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利用超声处理和深度学习从火中设计和制造材料。

Designing and fabricating materials from fire using sonification and deep learning.

作者信息

Milazzo Mario, Buehler Markus J

机构信息

Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm. 1-165, Cambridge, MA 02139, USA.

Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.

出版信息

iScience. 2021 Jul 16;24(8):102873. doi: 10.1016/j.isci.2021.102873. eCollection 2021 Aug 20.

DOI:10.1016/j.isci.2021.102873
PMID:34409268
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8361214/
Abstract

Fire has fascinated humankind since the prehistoric era. Rooted in the interactions between sound and flames, here we report a method to use fire for a variety of purposes, including sonification, art, and the design and manufacturing nature-inspired materials. We present a method to sonify fire, thereby offering a translation from the silent nature of flames, to represent audible information and to generate flame images. To realize material specimen derived from fire, we use the autoencoder to generate image stacks to yield continuous 3D geometries that are manufactured using 3D printing. This represents the first generation of nature-inspired materials from fire and can be a platform to be used for other natural phenomena in the quest for architectures, geometries, and design ideas, thus creating additional directions in artistic and scientific research through the creative manipulation of data with structural similarities across fields.

摘要

自史前时代以来,火就一直吸引着人类。基于声音与火焰之间的相互作用,我们在此报告一种将火用于多种用途的方法,包括声波化、艺术以及设计和制造受自然启发的材料。我们提出了一种使火产生声音的方法,从而实现从火焰的无声本质到表示可听信息并生成火焰图像的转换。为了实现源自火的材料样本,我们使用自动编码器生成图像堆栈,以产生连续的三维几何形状,然后通过3D打印进行制造。这代表了第一代受火启发的材料,并且可以成为一个平台,用于探索其他自然现象以获取架构、几何形状和设计理念,从而通过跨领域对具有结构相似性的数据进行创造性操作,在艺术和科学研究中开辟新的方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/f046ae1e774c/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/a236c2bf6757/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/bec8678625a7/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/9d291fd87f20/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/b22e0229da48/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/575f51aa5573/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/2767dd32014a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/035f0f9418e8/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/433110b2ddc0/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/8872ace0caa7/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/c527ef9d447a/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/f046ae1e774c/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/a236c2bf6757/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/bec8678625a7/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/9d291fd87f20/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/b22e0229da48/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/575f51aa5573/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/2767dd32014a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/035f0f9418e8/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/433110b2ddc0/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/8872ace0caa7/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/c527ef9d447a/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2827/8361214/f046ae1e774c/gr10.jpg

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Sonification based protein design using artificial intelligence, structure prediction, and analysis using molecular modeling.基于可听化技术,利用人工智能进行蛋白质设计、结构预测以及使用分子建模进行分析。
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Sounds interesting: can sonification help us design new proteins?
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Expert Rev Proteomics. 2019 Nov-Dec;16(11-12):875-879. doi: 10.1080/14789450.2019.1697236. Epub 2019 Nov 27.
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Meta-biomaterials.元生物材料。
Biomater Sci. 2019 Dec 17;8(1):18-38. doi: 10.1039/c9bm01247h.
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