An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery

Soofi, Yasaman J.; Gu, Yijia; Liu, J

doi:10.1016/j.commatsci.2023.112248

Computational Materials Science

2023

DOI: 10.1016/j.commatsci.2023.112248

|View full text |Cite

An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery

Yasaman J. Soofi

Yijia Gu

J Liu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial Intelligence in Metamaterial Informatics for Sonic Frequency Mechanical Identification Tags

Saatchi,

Lee,

Pandit

et al. 2024

Adv Funct Materials

View full text Add to dashboard Cite

Designing mechanical metamaterials to control wave propagation often requires extensive finite element analysis and discrete Fourier transform simulations before fabricating 3D printed structures and conducting experiments. Here, an alternative approach is presented to developing a metamaterial informatics framework by integrating dataset collection with artificial intelligence (AI), which can significantly accelerate the advancement of phononic wave chip technologies based on the triply periodic minimal surface (TPMS). Visualized data analysis is performed to evaluate the sensitivity of phononic band frequency numbers (BNF). Subsequently, various machine learning algorithms are compared for the prediction of sonic BNFs to create a unique identificable encoded mechanical identification tag (EMIT) interacting with sound waves. Then, for the mechanical decoding part with the help of acoustic analogy, a novel concept technology is developed that integrates 3D‐printed EMITs with a deep‐learning audio classifier for the ownership identification of instruments. Underwater application is discussed further for civil accident investigations, such as echolocating missing aircraft, divers, sunken ships, and containers with valuable cargo. These TPMS‐based EMITs represent the first‐generation passive sonic frequency identification (SFID) transponder‐tags, marking the advent of SFID transponder systems.

show abstract

Artificial Intelligence in Metamaterial Informatics for Sonic Frequency Mechanical Identification Tags

Saatchi,

Lee,

Pandit

et al. 2024

Adv Funct Materials

View full text Add to dashboard Cite

show abstract

Assessment of Nano-Imprinting Process in CuZr Amorphous Films Through Combination of Machine Learning and Molecular Dynamics

Sayed

Al-Sakhnini

Al-Zubaidi³

et al. 2023

J. Electron. Mater.

View full text Add to dashboard Cite

A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning

Zhang,

Gao,

Liu

et al. 2024

JOM

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery

Cited by 6 publications

References 13 publications

Artificial Intelligence in Metamaterial Informatics for Sonic Frequency Mechanical Identification Tags

Artificial Intelligence in Metamaterial Informatics for Sonic Frequency Mechanical Identification Tags

Assessment of Nano-Imprinting Process in CuZr Amorphous Films Through Combination of Machine Learning and Molecular Dynamics

A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning

Contact Info

Product

Resources

About