Communications Materials (Nov 2022)
Machine-learning for designing nanoarchitectured materials by dealloying
- Chonghang Zhao,
- Cheng-Chu Chung,
- Siying Jiang,
- Marcus M. Noack,
- Jiun-Han Chen,
- Kedar Manandhar,
- Joshua Lynch,
- Hui Zhong,
- Wei Zhu,
- Phillip Maffettone,
- Daniel Olds,
- Masafumi Fukuto,
- Ichiro Takeuchi,
- Sanjit Ghose,
- Thomas Caswell,
- Kevin G. Yager,
- Yu-chen Karen Chen-Wiegart
Affiliations
- Chonghang Zhao
- Department of Materials Science and Chemical Engineering, Stony Brook University
- Cheng-Chu Chung
- Department of Materials Science and Chemical Engineering, Stony Brook University
- Siying Jiang
- Department of Applied Mathematics and Statistics, Stony Brook University
- Marcus M. Noack
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory
- Jiun-Han Chen
- Independent Researcher
- Kedar Manandhar
- Department of Materials Science and Engineering, University of Maryland
- Joshua Lynch
- National Synchrotron Light Source II, Brookhaven National Laboratory
- Hui Zhong
- Department of Joint Photon Science Institute, Stony Brook University
- Wei Zhu
- Department of Applied Mathematics and Statistics, Stony Brook University
- Phillip Maffettone
- National Synchrotron Light Source II, Brookhaven National Laboratory
- Daniel Olds
- National Synchrotron Light Source II, Brookhaven National Laboratory
- Masafumi Fukuto
- National Synchrotron Light Source II, Brookhaven National Laboratory
- Ichiro Takeuchi
- Department of Materials Science and Engineering, University of Maryland
- Sanjit Ghose
- National Synchrotron Light Source II, Brookhaven National Laboratory
- Thomas Caswell
- National Synchrotron Light Source II, Brookhaven National Laboratory
- Kevin G. Yager
- Center for Functional Nanomaterials, Brookhaven National Laboratory
- Yu-chen Karen Chen-Wiegart
- Department of Materials Science and Chemical Engineering, Stony Brook University
- DOI
- https://doi.org/10.1038/s43246-022-00303-w
- Journal volume & issue
-
Vol. 3,
no. 1
pp. 1 – 12
Abstract
Nanoporous metals produced by metal agent dealloying are attractive for multiple applications. Here, a machine learning-augmented framework is reported for predicting, synthesizing and characterizing ternary systems for dealloying.
