Data-driven discovery of a formation prediction rule on high-entropy ceramics

Yan, Yonggang; Pei, Zongrui; Gao, Michael C.; Wang, Kun

doi:10.1016/j.actamat.2023.118955

Cited by 10 publications

(3 citation statements)

References 118 publications

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“…and Spiridigliozzi et al 4 .investigated the single-phase synthesis of complex oxides in perovskite and fluorite structure, respectively. Yan et al 5 . found that the formation of complex single-phase borides depends strongly on the deviation of the metal-B bond lengths.…”

Section: Introductionmentioning

confidence: 99%

Explaining the entropy forming ability for carbides with the effective atomic size mismatch

Kretschmer,

Mayrhofer

2024

Sci Rep

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To quickly screen for single-phased multi-principal-element materials, a so-called entropy forming ability (EFA) parameter is sometimes used as a descriptor. The larger the EFA, the larger is the propensity to form a single-phase structure. We have investigated this EFA descriptor with atomic relaxations in special-quasi-random structures and discovered that the EFA correlates inversely with the lattice distortion. Large effective atomic size differences lead to multi-phase compounds, and little size differences to single-phase compounds. Instead of configurational entropy, we therefore demonstrate the applicability of the Hume-Rothery rules to phase stability of solid solutions even in compositionally complex ceramics.

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Section: Introductionmentioning

confidence: 99%

Explaining the entropy forming ability for carbides with the effective atomic size mismatch

Kretschmer,

Mayrhofer

2024

Sci Rep

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“…The data‐driven strategies with a combination of high‐throughput synthesis experiments and machine learning (ML) have recently been developed to be an efficient way to explore the synthesizability of high‐entropy ceramics 17–21 . Efforts have been successfully made hitherto in the field of high‐entropy borides.…”

Section: Introductionmentioning

confidence: 99%

“…However, the inconsistency of synthesis methods and limited descriptors are detrimental to the accuracy of their prediction. On the basis of 165 samples synthesized by the high‐throughput spark plasma sintering approach and the ML model with 12 descriptors, Yan et al 20 . proposed an empirical rule to dictate the synthesizability of single‐phase HEB 2 with an accuracy of 93.3%.…”

Section: Introductionmentioning

confidence: 99%

Accelerating the discovery of high‐entropy hexaborides by data‐driven prediction: From equimolar to non‐equimolar

Meng,

Tang,

Bai

et al. 2024

J Am Ceram Soc.

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Estimating the synthesizability is the prerequisite to discovering novel high‐entropy ceramics with exotic properties. Herein, combined with the high‐throughput experiments and machine learning (ML) methods, the synthesizability of high‐entropy hexaborides (HEB6) is investigated. To construct the database, 100 equimolar quinary HEB6 samples synthesized using a self‐developed high‐throughput solid‐state reaction technique and 20 potential synthesizability descriptors calculated from fundamental parameters of constituent precursors are simultaneously collected. By employing the ML and the genetic algorithms, an optimal model consisting of five synthesizability descriptors (, , , , and ) is determined for predicting the synthesizability of equimolar HEB6 with a high validation accuracy (93.0%), and 7 005 new equimolar quinary HEB6 are then proposed. Moreover, the applicability of our established model on non‐equimolar HEB6 is explored by the prediction of 30 synthesizability diagrams of non‐equimolar quinary HEB6. A high accuracy of 90.9% is further validated by the synthesis experiments of 11 non‐equimolar HEB6 candidates. Our work establishes an effective ML model for assessing the synthesizability of both equimolar and non‐equimolar HEB6 and paves a promising way to accelerate the discovery of new synthetic accessible high‐entropy ceramics.

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Design of superhard high-entropy diborides via high-throughput DFT and thermodynamics calculations

Liu,

Qin,

Zhang

et al. 2024

Ceramics International

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Data-driven discovery of a formation prediction rule on high-entropy ceramics

Cited by 10 publications

References 118 publications

Explaining the entropy forming ability for carbides with the effective atomic size mismatch

Explaining the entropy forming ability for carbides with the effective atomic size mismatch

Accelerating the discovery of high‐entropy hexaborides by data‐driven prediction: From equimolar to non‐equimolar

Design of superhard high-entropy diborides via high-throughput DFT and thermodynamics calculations

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