Using Machine Learning to Predict and Understand Complex Self‐Assembly Behaviors of a Multicomponent Nanocomposite-Reference-Cited by-同舟云学术

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Using Machine Learning to Predict and Understand Complex Self‐Assembly Behaviors of a Multicomponent Nanocomposite

Published:2022-07-08 Issue:32 Volume:34 Page:2203168
ISSN:0935-9648
Container-title:Advanced Materials
language:en
Short-container-title:Advanced Materials

Author:

Vargo Emma¹²^ORCID,Dahl Jakob C.²³^ORCID,Evans Katherine M.²³,Khan Tasneem³,Alivisatos Paul¹²³^ORCID,Xu Ting¹²³^ORCID

Affiliation:

1. Department of Materials Science and Engineering University of California, Berkeley Berkeley CA 94720 USA

2. Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA

3. Department of Chemistry University of California, Berkeley Berkeley CA 94720 USA

Funder

National Science Foundation

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.202203168

Reference37 articles.

1. Review of Nanocomposite Dielectric Materials With High Thermal Conductivity

2. Enabling Strategies in Organic Electronics Using Ordered Block Copolymer Nanostructures

3. Orbital Angular Momentum from Self‐Assembled Concentric Nanoparticle Rings

4. Improving Faraday rotation performance with block copolymer and FePt nanoparticle magneto-optical composite

5. Improved effective medium approach: Application to metal nanocomposites

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3. Computationally guided design and synthesis of dual‐drug loaded polymeric nanoparticles for combination therapy;Aggregate;2024-06-03

4. Machine learning for analyses and automation of structural characterization of polymer materials;Progress in Polymer Science;2024-06

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