Machine learning of phases and mechanical properties in complex concentrated alloys
The mechanical properties of complex concentrated alloys (CCAs) depend on their formed
phases and corresponding microstructures. The data-driven prediction of the phase …
phases and corresponding microstructures. The data-driven prediction of the phase …
Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys
The difference in the mechanical behaviors of dilute solid solutions, complex solid solutions
and their corresponding strengthening mechanisms, is an evolving field of study. An …
and their corresponding strengthening mechanisms, is an evolving field of study. An …
Machine learning assisted prediction of the Young's modulus of compositionally complex alloys
We identify compositionally complex alloys (CCAs) that offer exceptional mechanical
properties for elevated temperature applications by employing machine learning (ML) in …
properties for elevated temperature applications by employing machine learning (ML) in …
Machine learning for phase selection in multi-principal element alloys
Multi-principal element alloys (MPEAs) especially high entropy alloys have attracted
significant attention and resulted in a novel concept of designing metal alloys via exploring …
significant attention and resulted in a novel concept of designing metal alloys via exploring …
An informatics guided classification of miscible and immiscible binary alloy systems
The classification of miscible and immiscible systems of binary alloys plays a critical role in
the design of multicomponent alloys. By mining data from hundreds of experimental phase …
the design of multicomponent alloys. By mining data from hundreds of experimental phase …
Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
Materials informatics employs machine learning (ML) models to map the relationship
between a targeted property and various materials descriptors, providing new avenues to …
between a targeted property and various materials descriptors, providing new avenues to …
Chemical short range order strengthening in BCC complex concentrated alloys
E Antillon, C Woodward, SI Rao, B Akdim - Acta Materialia, 2021 - Elsevier
Atomistic methods are used to anneal two body-centered cubic (BCC) chemically complex
alloys (CCAs) in order to assess the effect of chemical short-range order on alloy strength …
alloys (CCAs) in order to assess the effect of chemical short-range order on alloy strength …
Machine learned feature identification for predicting phase and Young's modulus of low-, medium-and high-entropy alloys
The growth in the interest and research on high-entropy alloys (HEAs) over the last decade
is due to their unique material phases responsible for their remarkable structural properties …
is due to their unique material phases responsible for their remarkable structural properties …
[HTML][HTML] Current application status of multi-scale simulation and machine learning in research on high-entropy alloys
D Jiang, L Xie, L Wang - Journal of Materials Research and Technology, 2023 - Elsevier
High-entropy alloys (HEAs) have garnered significant attention across various fields owing
to their unique design incorporating multi-principal elements and remarkable …
to their unique design incorporating multi-principal elements and remarkable …
Supervised machine learning-based multi-class phase prediction in high-entropy alloys using robust databases
This work evaluated the phase prediction capability of high entropy alloys using four
supervised machine learning models K-Nearest Neighbors (KNN), Multinomial Regression …
supervised machine learning models K-Nearest Neighbors (KNN), Multinomial Regression …