Machine Learning Predictions of Block Copolymer Self‐Assembly-Reference-Cited by-同舟云学术

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Machine Learning Predictions of Block Copolymer Self‐Assembly

Published:2020-11-18 Issue:52 Volume:32 Page:2005713
ISSN:0935-9648
Container-title:Advanced Materials
language:en
Short-container-title:Adv. Mater.

Author:

Tu Kun‐Hua¹,Huang Hejin¹^ORCID,Lee Sangho¹^ORCID,Lee Wonmoo¹,Sun Zehao¹^ORCID,Alexander‐Katz Alfredo¹^ORCID,Ross Caroline A.¹^ORCID

Affiliation:

1. Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA

Funder

National Science Foundation

Materials Research Science and Engineering Center, Harvard University

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.202005713

Reference72 articles.

1. Functional nanostructured materials based on self-assembly of block copolymers

2. Two-Dimensional Pattern Formation Using Graphoepitaxy of PS-b-PMMA Block Copolymers for Advanced FinFET Device and Circuit Fabrication

3. Block copolymers and conventional lithography

4. Sub-5 nm Domains in Ordered Poly(cyclohexylethylene)-block-poly(methyl methacrylate) Block Polymers for Lithography

5. Directed Self-Assembly and Pattern Transfer of Five Nanometer Block Copolymer Lamellae

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5. The triumvirate of effective and rapid synthesis, analysis, and artificial intelligence to explore the structure-property relationship of copolymers;Giant;2024-03

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