File(s) not publicly available
Searching for chromate replacements using natural language processing and machine learning algorithms
journal contribution
posted on 2023-02-14, 04:12 authored by S Zhao, N BirbilisAbstractThe past few years have seen the application of machine learning utilised in the exploration of materials. As in many fields of research—the vast majority of knowledge is published as text, which poses challenges in either a consolidated or statistical analysis across studies and reports. To address this issue, the application of natural language processing (NLP) has been explored in several studies to date. In the present work, we have employed the Word2Vec model, previously explored by others, and the BERT model—applying them towards the search for chromate replacements in the field of corrosion protection. From a database of over 80 million records, a down-selection of 5990 papers focused on the topic of corrosion protection were examined using NLP. This study demonstrates it is possible to extract knowledge from the automated interpretation of the scientific literature and achieve expert human-level insights.
History
Journal
npj Materials DegradationVolume
7Article number
ARTN 2Publisher DOI
ISSN
2397-2106eISSN
2397-2106Language
EnglishPublication classification
C1 Refereed article in a scholarly journalIssue
1Publisher
NATURE PORTFOLIOUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC