logo
ResearchBunny Logo
Abstract
This study explores the use of natural language processing (NLP) and machine learning to identify chromate replacements for corrosion protection. Using Word2Vec and BERT models, the researchers analyzed a database of over 80 million records, focusing on 5990 papers related to corrosion protection. The study demonstrates the feasibility of extracting expert-level insights from scientific literature using automated interpretation, highlighting the potential of NLP in materials science research.
Publisher
npj Materials Degradation
Published On
Jan 06, 2023
Authors
Shujing Zhao, Nick Birbilis
Tags
natural language processing
machine learning
corrosion protection
chromate replacements
scientific literature
Word2Vec
BERT

Related Publications

Explore these studies to deepen your understanding of the subject.

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.

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms
Engineering and Technology

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

M. A. Khan, B. Asad, 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.

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications
Computer Science

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

S. H. Snyder, P. A. Vignaux, 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