Machine learning-based structure–property predictions in silica aerogels-Reference-Cited by-同舟云学术

Machine learning-based structure–property predictions in silica aerogels

Published:2021 Issue:31 Volume:17 Page:7350-7358
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Abdusalamov Rasul¹²³⁴,Pandit Prakul⁵⁶⁷⁸⁴,Milow Barbara⁵⁶⁷⁸⁴,Itskov Mikhail¹²³⁴,Rege Ameya⁵⁶⁷⁸⁴^ORCID

Affiliation:

1. Department of Continuum Mechanics

2. RWTH Aachen University

3. Aachen

4. Germany

5. Department of Aerogels and Aerogel Composites

6. Institute of Materials Research

7. German Aerospace Center

8. Cologne

Abstract

An artificial neural network is developed to predict the fractal properties of silica aerogels, modelled via diffusion-limited cluster–cluster aggregation, and then inverted for reconstructing an optimised network for a target fractal dimension.

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SM/D1SM00307K

Reference19 articles.

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5. An efficient Monte Carlo strategy for elasto-plastic structures based on recurrent neural networks

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