Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking-Reference-Cited by-同舟云学术

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Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking

Published:2019-08-12 Issue:1 Volume:5 Page:
ISSN:2057-3960
Container-title:npj Computational Materials
language:en
Short-container-title:npj Comput Mater

Author:

Griffin Lee A.^ORCID,Gaponenko Iaroslav,Zhang Shujun^ORCID,Bassiri-Gharb Nazanin^ORCID

Funder

National Science Foundation

United States Department of Defense | Defense Threat Reduction Agency

Center for the Science and Technology of Advanced Materials and Interfaces (STAMI) at the Georgia Institute of Technology

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Mechanics of Materials,General Materials Science,Modelling and Simulation

Link

http://www.nature.com/articles/s41524-019-0222-z.pdf

Reference53 articles.

1. Zeng, W. et al. Fiber-based wearable electronics: a review of materials, fabrication, devices, and applications. Adv. Mater. 26, 5310–5336 (2014).

2. Amjadi, M., Kyung, K.-U., Park, I. & Sitti, M. Stretchable, skin-mountable, and wearable strain sensors and their potential applications: a review. Adv. Funct. Mater. 26, 1678–1698 (2016).

3. Ramadan, K. S., Sameoto, D. & Evoy, S. A review of piezoelectric polymers as functional materials for electromechanical transducers. Smart Mater. Struct. 23, 033001 (2014).

4. Cheng, F., Liang, J., Tao, Z. & Chen, J. Functional materials for rechargeable batteries. Adv. Mater. 23, 1695–1715 (2011).

5. Guo, X. et al. Recent developments of aprotic lithium-oxygen batteries: functional materials determine the electrochemical performance. Sci. Bull. 62, 442–452 (2017).

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