Machine Learning Enabled Prediction of High Stiffness 2D Materials-Reference-Cited by-同舟云学术

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Machine Learning Enabled Prediction of High Stiffness 2D Materials

Published:2024-01-26 Issue:2 Volume:6 Page:729-736
ISSN:2639-4979
Container-title:ACS Materials Letters
language:en
Short-container-title:ACS Materials Lett.

Author:

Nadella Hema Rajesh¹,Mukherjee Sankha²^ORCID,Anand Abu³,Singh Chandra Veer¹³^ORCID

Affiliation:

1. Mechanical & Industrial Engineering, University of Toronto, Toronto, ON M5S3G8, Canada

2. Metallurgical & Materials Engineering, IIT Kharagpur, West Bengal 721302, India

3. Materials Science & Engineering, University of Toronto, Toronto, ON M5S3E4, Canada

Funder

University of Toronto

Natural Sciences and Engineering Research Council of Canada

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsmaterialslett.3c01322

Reference44 articles.

1. 2D materials and van der Waals heterostructures

2. Measurement of the Elastic Properties and Intrinsic Strength of Monolayer Graphene

3. Review of borophene and its potential applications

4. Mechanical properties of two-dimensional materials and their applications

5. A Review of the Synthesis, Properties, and Applications of 2D Materials

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation;Physica Scripta;2024-08-09

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