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Searching for chromate replacements using natural language processing and machine learning algorithms

Engineering and Technology

Searching for chromate replacements using natural language processing and machine learning algorithms

S. Zhao and N. Birbilis

This study, conducted by Shujing Zhao and Nick Birbilis, showcases how natural language processing and machine learning can uncover alternatives to chromate for corrosion protection. By analyzing an extensive database of scientific literature, the research reveals how automated interpretation can yield expert insights in materials science.

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Playback language: English
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

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