Machine-learning for designing nanoarchitectured materials by dealloying

Year: 2022

Type: article

Abstract: Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.

Source: Communications Materials

DOI: 10.1038/s43246-022-00303-w

Authors: 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

Institutions: Brookhaven National Laboratory, National Synchrotron Light Source II, Stony Brook University, Lawrence Berkeley National Laboratory, University of Maryland, College Park, Center for Functional Nanomaterials

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,190

Funders: National Science Foundation, U.S. Department of Energy, Stony Brook University, Office of Science, Division of Materials Research, Basic Energy Sciences, Advanced Scientific Computing Research, Brookhaven National Laboratory

OpenAlex ID: W4308736201