24 citations · 45 across the 13 of their papers we have counts for
13 papers
A primitive machine learning tool for the mechanical property prediction of multiple principal element alloys
R. Tan, Z. Li, S. Zhao +1
Multi-principal element alloys (MPEAs) are produced by combining metallic elements in what is a diverse range of proportions. MPEAs reported to date have revealed promising perform…
A Computational Approach for Mapping Electrochemical Activity of Multi-Principal Element Alloys
Jodie A. Yuwono, Xinyu Li, Tyler D. Doležal +4
Multi principal element alloys (MPEAs) comprise a unique class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant…
Searching for chromate replacements using natural language processing and machine learning algorithms
Shujing Zhao, Nick Birbilis
The past few years has seen the application of machine learning utilised in the exploration of new materials. As in many fields of research - the vast majority of knowledge is publ…
RustSEG -- Automated segmentation of corrosion using deep learning
B. Burton, W. T. Nash, N. Birbilis
The inspection of infrastructure for corrosion remains a task that is typically performed manually by qualified engineers or inspectors. This task of inspection is laborious, slow,…
cardiGAN: A Generative Adversarial Network Model for Design and Discovery of Multi Principal Element Alloys
Z. Li, W. T. Nash, S. P. O Brien +3
Multi-principal element alloys (MPEAs), inclusive of high entropy alloys (HEAs), continue to attract significant research attention owing to their potentially desirable properties.…
Aluminium Alloy Design and Discovery using Machine Learning
J. Mangos, N. Birbilis
The traditional design and development of metallic alloys has taken a hill-climbing approach to date, with incremental advances. Throughout the last century, aluminium (Al) alloy d…