logo
ResearchBunny Logo
A rapid and effective method for alloy materials design via sample data transfer machine learning

Engineering and Technology

A rapid and effective method for alloy materials design via sample data transfer machine learning

L. Jiang, Z. Zhang, et al.

Discover a groundbreaking method for alloy material design utilizing data transfer learning, as demonstrated by Lei Jiang, Zhihao Zhang, Hao Hu, Xingqun He, Huadong Fu, and Jianxin Xie. This research introduces an innovative aluminum alloy (E2 alloy) that combines ultra-strength with high toughness through a novel treatment approach, proving the potential of advanced data-driven techniques in material science.

00:00
00:00
Playback language: English
Citation Metrics
Citations
0
Influential Citations
0
Reference Count
0

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

Related Publications

Explore these studies to deepen your understanding of the subject.

HIDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data
Medicine and Health

HIDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data

A. Goeva, M. Dolan, et al.

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets
Medicine and Health

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

X. Xue, S. Chinnaperumal, et al.

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol
Engineering and Technology

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol

T. Hayasaka, A. Lin, et al.

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects
Medicine and Health

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

A. Mcdowell, J. Kang, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny