Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

Zhang, Yan; Wen, Cheng; Wang, Cynthia; Antonov, Stoichko; Xue, Dezhen; Bai, Yang

doi:10.1016/j.actamat.2019.11.067

Cited by 236 publications

(102 citation statements)

References 48 publications

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“…Zhang et al [111] developed a data-driven model to choose elements that may exhibit high possibility to form high-entropy sulfides. In comparison to HECs, more studies on predicting the formation ability have been done in HEAs [166,[268][269][270][271][272][273]. These works provide guidelines to develop predictive models for HECs, for example, approximating configurational energies by only considering pairwise interactions.…”

Section: Stability Predictionmentioning

confidence: 99%

High-entropy ceramics: Present status, challenges, and a look forward

et al. 2021

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High-entropy ceramics (HECs) are solid solutions of inorganic compounds with one or more Wyckoff sites shared by equal or near-equal atomic ratios of multi-principal elements. Although in the infant stage, the emerging of this new family of materials has brought new opportunities for material design and property tailoring. Distinct from metals, the diversity in crystal structure and electronic structure of ceramics provides huge space for properties tuning through band structure engineering and phonon engineering. Aside from strengthening, hardening, and low thermal conductivity that have already been found in high-entropy alloys, new properties like colossal dielectric constant, super ionic conductivity, severe anisotropic thermal expansion coefficient, strong electromagnetic wave absorption, etc., have been discovered in HECs. As a response to the rapid development in this nascent field, this article gives a comprehensive review on the structure features, theoretical methods for stability and property prediction, processing routes, novel properties, and prospective applications of HECs. The challenges on processing, characterization, and property predictions are also emphasized. Finally, future directions for new material exploration, novel processing, fundamental understanding, in-depth characterization, and database assessments are given.

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Section: Stability Predictionmentioning

confidence: 99%

High-entropy ceramics: Present status, challenges, and a look forward

et al. 2021

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“…27 However, the reliability of the CALPHAD approach depends on the accuracy of the database, and multicomponent systems are remaining vastly unexplored. Recently, more research focused on machine learning (ML), [167][168][169][170][171][172][173][174][175][176][177][178][179][180] which has inherent advantages over traditional modeling and seems more suitable for discovering high-entropy systems. Kaufmann et al 175 proposed a novel high-throughput ''ML-HEA'' approach for predicting the ability of solid-solution formation.…”

Section: Computational Techniquesmentioning

confidence: 99%

High-entropy energy materials: challenges and new opportunities

Wang

et al. 2021

Energy Environ. Sci.

323

197

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“…; and alternative approaches such as CALPHAD, ab initio, density functional theory (DFT) and so on; albeit, the accuracy of prediction becomes important [42,43]. The high entropy alloy phase formation is not entirely dependent on any fixed or single approach or rules [44][45][46][47][48]. The phase formation is dependent on the selection parameters such as solid solution (SS) phase, intermetallic (IM) compound and mixed phase (SS + IM).…”

Section: Phase Prediction and Electronic Interactionsmentioning

confidence: 99%

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

Katiyar

Goel

2021

emergent mater.

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The high entropy alloys have become the most intensely researched materials in recent times. They offer the flexibility to choose a large array of metallic elements in the periodic table, a combination of which produces distinctive desirable properties that are not possible to be obtained by the pristine metals. Over the past decade, a myriad of publications has inundated the aspects of materials synthesis concerning HEA. Hitherto, the practice of HEA development has largely relied on a trial-and-error basis, and the hassles associate with this effort can be reduced by adopting a machine learning approach. This way, the “right first time” approach can be adopted to deterministically predict the right combination and composition of metallic elements to obtain the desired functional properties. This article reviews the latest advances in adopting machine learning approaches to predict and develop newer compositions of high entropy alloys. The review concludes by highlighting the newer applications areas that this accelerated development has enabled such that the HEA coatings can now potentially be used in several areas ranging from catalytic materials, electromagnetic shield protection and many other structural applications.

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Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

Cited by 236 publications

References 48 publications

High-entropy ceramics: Present status, challenges, and a look forward

High-entropy ceramics: Present status, challenges, and a look forward

High-entropy energy materials: challenges and new opportunities

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

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