High entropy alloys towards industrial applications: High-throughput screening and experimental investigation

Conway, Patrick L. J.; Klaver, T.P.C.; Steggo, Jacob; Ghassemali, Ehsan

doi:10.1016/j.msea.2021.142297

Cited by 37 publications

(12 citation statements)

References 83 publications

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“…The number of CALPHAD calculations also depends on the temperature range and the respective step size. In the algorithm we have been recently developing (Conway et al, 2022), the built compositions will be fed to the ThermoCalc software as a macro file (Python interface). Depending on the desired results, different modules of the ThermoCalc could be engaged in HT calculations.…”

Section: Phase Diagram Calculationsmentioning

confidence: 99%

“…These results as a dataset will be used for ranking the compositions into islands of selected compositions. Based on the set criteria for alloy design and expected properties, the center of these compositional islands is selected (Conway et al, 2022). It is important to note that the screening may contain false-negative results (false rejection of compositions with development potential), which can be a concern since the very spirit of identifying potentially exceptional alloys may be hampered.…”

Section: Phase Diagram Calculationsmentioning

confidence: 99%

“…Their method can be used as either a visual tool for elemental selection or an alternative for visualisation of the HT-CALPHAD results. An intermediate alternative we have proposed recently (Conway et al, 2022) includes binary visualisation of pair of elements.…”

Section: Phase Diagram Calculationsmentioning

confidence: 99%

“…New codings have been developed to facilitate such calculations in conventional high-performance computers. Recently, we further developed the screening method initially proposed by Klaver et al (2018), for screening and modifying the composition of equimolar HEAs to reduce the elemental costs and alloy density while maintaining the mechanical properties of the equimolar HEAs (Conway et al, 2022). Python coding was used to build the composition sets and create the macro files for ThermoCalc software.…”

Section: Required Computational Powers and Timementioning

confidence: 99%

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High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy

Ghassemali

Conway

2022

Front. Mater.

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Introduction of high entropy alloys or multi-principal element alloys around 15 years ago motivated revising conventional alloy design strategies and proposed new ways for alloy development. Despite significant research since then, the potential for new material discoveries using the MPEA concept has hardly been scratched. Given the number of available elements and the vastness of possible composition combinations, an unlimited number of alloys are waiting to be investigated! Discovering novel high-performance materials can be like finding a needle in a haystack, which demands an enormous amount of time and computational capacity. To overcome the challenge, a systematic approach is essential to meet the growing demand for developing novel high-performance or multifunctional materials. This article aims to briefly review the challenges, recent progress and gaps, and future outlook in accelerated alloy development, with a specific focus on computational high-throughput (HT) screening methods integrated with the Calculation of Phase Diagrams (CALPHAD) technique.

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Section: Phase Diagram Calculationsmentioning

confidence: 99%

Section: Phase Diagram Calculationsmentioning

confidence: 99%

Section: Phase Diagram Calculationsmentioning

confidence: 99%

Section: Required Computational Powers and Timementioning

confidence: 99%

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High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy

Ghassemali

Conway

2022

Front. Mater.

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“…HT techniques are essential for scientists to efficiently generate large databases and the subsequent information extraction [33,34] . For HEAs with huge composition space, HT techniques can be used to great effect for the discovery and development of HEAs [35] .…”

Section: Ht Research For Heasmentioning

confidence: 99%

High-entropy alloy catalysts: high-throughput and machine learning-driven design

Chen¹,

Chen²,

Xue³

et al. 2022

J Mater Inf

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High-entropy alloy (HEA) catalysts have recently attracted worldwide research interest due to their promising catalytic performance. Most current studies focus on designing HEA catalysts through trial-and-error methods. This produces scattered data and is not conducive to obtaining a fundamental understanding of the structure-property-performance relationships for HEA catalysts, thereby hindering their rational design. High-throughput (HT) techniques and machine learning (ML) methods show significant potential in generating, processing and analyzing databases with a vast amount of data, providing a new strategy for the further development of HEA catalysts. In this review, we summarize the recent literature on HT techniques for HEA synthesis, characterization and performance testing. We also review the ML models that are used to process and analyze existing databases to accelerate the discovery of HEA catalysts. Finally, the potential challenges and perspectives of HT techniques and ML models are presented to accelerate the discovery of new HEA catalysts and promote their development.

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Combinatorial Design of Novel Multiprincipal Element Alloys Using Experimental Techniques

Gubicza

2023

Adv Eng Mater

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Multiprincipal element alloys (MPEAs) including high‐entropy alloys are in the focus of materials science since many MPEA compositions exhibit outstanding mechanical and physical properties. These alloys contain 3–6 constituents with similar fractions; therefore, MPEAs correspond to the unexplored middle part of the phase diagrams. The phase composition and the performance of MPEAs are strongly influenced by their chemistry. Thus, it is very important to reveal the correlation between the composition, the structure, and the properties of MPEAs. On the other hand, this task is challenging due to the vast composition space of these alloys. The processing and characterization of combinatorial specimens can yield a fast mapping of compositional libraries of MPEAs. Herein, the experimental techniques developed for manufacturing and investigation of these alloys are overviewed and the results are critically discussed. Finally, further development directions in the field of combinatorial MPEAs are recommended in order to improve the study of the different alloy compositions.

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High entropy alloys towards industrial applications: High-throughput screening and experimental investigation

Cited by 37 publications

References 83 publications

High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy

High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy

High-entropy alloy catalysts: high-throughput and machine learning-driven design

Combinatorial Design of Novel Multiprincipal Element Alloys Using Experimental Techniques

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