1 citations · 1 across the 4 of their papers we have counts for
4 papers
Revolutionising inverse design of magnesium alloys through generative adversarial networks
Marzie Ghorbani, Zhipeng Li, Nick Birbilis
The utility of machine learning (ML) techniques in materials science has accelerated materials design and discovery. However, the accuracy of ML models - particularly deep neural n…
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…