2021
DOI: 10.1016/j.mattod.2021.03.018
|View full text |Cite
|
Sign up to set email alerts
|

Accelerated and conventional development of magnetic high entropy alloys

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
38
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
9
1

Relationship

1
9

Authors

Journals

citations
Cited by 140 publications
(38 citation statements)
references
References 256 publications
0
38
0
Order By: Relevance
“…The HEAs are almost fully magnetically saturated at fields of 500 mT. Figure 1 B shows a comparison of M S and H C for AlCoFeNi and AlCo 0.5 Cr 0.5 FeNi HEAs with other HEAs and benchmark soft magnetic materials ( Borkar et al., 2017 ; Chaudhary et al., 2021 ; Dasari et al., 2020 ; Li et al., 2017 ; Zhang et al., 2013 ). The magnetic properties of the HEAs that we have studied are similar to others.…”
Section: Resultsmentioning
confidence: 99%
“…The HEAs are almost fully magnetically saturated at fields of 500 mT. Figure 1 B shows a comparison of M S and H C for AlCoFeNi and AlCo 0.5 Cr 0.5 FeNi HEAs with other HEAs and benchmark soft magnetic materials ( Borkar et al., 2017 ; Chaudhary et al., 2021 ; Dasari et al., 2020 ; Li et al., 2017 ; Zhang et al., 2013 ). The magnetic properties of the HEAs that we have studied are similar to others.…”
Section: Resultsmentioning
confidence: 99%
“…3D) illustrates that the weight coefficient of the VEC to decide the phase structure gradually declines, accompanied by the increase of the weight coefficient of the mixing enthalpy, which breaks the traditional rule of VEC acting as only the high impact feature. 25,26 Hence, the formation mechanism of multi-phases is extremely complex, and this result gives avenues towards the multi-mechanism criterion that beats the single rule for precise structural design.…”
Section: Phase Predictionmentioning
confidence: 99%
“…Furthermore, a combinatorial computational method is proposed to develop the new magnetic MPEAs. High-throughput method is utilized to screen out promising magnetic MPEAs, and then machine learning, additive manufacturing and CALPHAD methods are used to further predict the magnetic property (Chaudhary et al, 2021). Using DFT calculation and machine learning, *OH and *O adsorption energies on the surface of IrPdPtRhRu MPEA can be accurately predicted, which provids a useful strategy to discover the new MPEAs with outstanding catalytic activity (Batchelor et al, 2019).…”
Section: Machine Learningmentioning
confidence: 99%