Machine learning for high-entropy alloys: Progress, challenges and opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …
mechanical properties and the vast compositional space for new HEAs. However …
A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion
Laser powder bed fusion (LPBF) as one of the most promising additive manufacturing (AM)
technologies, has been widely used to produce metal parts and applied in fields such as …
technologies, has been widely used to produce metal parts and applied in fields such as …
A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
Trapped by time-consuming traditional trial-and-error methods and vast untapped
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …
Machine learning paves the way for high entropy compounds exploration: challenges, progress, and outlook
Abstract Machine learning (ML) has emerged as a powerful tool in the research field of high
entropy compounds (HECs), which have gained worldwide attention due to their vast …
entropy compounds (HECs), which have gained worldwide attention due to their vast …
Data-augmented modeling for yield strength of refractory high entropy alloys: A bayesian approach
Refractory high entropy alloys (RHEAs) have gained significant attention in recent years as
potential replacements for Ni-based superalloys in gas turbine applications. Improving their …
potential replacements for Ni-based superalloys in gas turbine applications. Improving their …
Machine learning accelerates the materials discovery
J Fang, M Xie, X He, J Zhang, J Hu, Y Chen… - Materials Today …, 2022 - Elsevier
As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …
technology becomes more and more accessible, the material design method based on …
[HTML][HTML] Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random forest regression model
S Kwak, J Kim, H Ding, X Xu, R Chen, J Guo… - Journal of Materials …, 2022 - Elsevier
The mechanical properties of a directionally solidified (DS) TiAl alloy were predicted through
a random forest regression (RFR) machine learning algorithm. The prediction results were …
a random forest regression (RFR) machine learning algorithm. The prediction results were …
Machine learning studies for magnetic compositionally complex alloys: A critical review
Soft magnetic alloys play a critical role in power conversion, magnetic sensing, magnetic
storage and electric actuating, which are fundamental components of modern technological …
storage and electric actuating, which are fundamental components of modern technological …
[HTML][HTML] Machine learning assisted modelling and design of solid solution hardened high entropy alloys
X Huang, C Jin, C Zhang, H Zhang, H Fu - Materials & Design, 2021 - Elsevier
High entropy alloys (HEAs) are considered as a way to unlock the unlimited potentials of
materials during material design, where solid solution hardening (SSH) is one of the major …
materials during material design, where solid solution hardening (SSH) is one of the major …
Frontiers in high entropy alloys and high entropy functional materials
Owing to their exceptional properties, high-entropy alloys (HEAs) and high-entropy materials
have emerged as promising research areas and shown diverse applications. Here, the …
have emerged as promising research areas and shown diverse applications. Here, the …