Electronic structures and strengthening mechanisms of superhard high-entropy diborides

Yao, Gang; Wang, William Yi; Li, Pei-Xuan; Ren, Ke; Lu, Jiaqi; Gao, Xingyu; Lin, De-Ye; Wang, Jun; Wang, Yiguang; Song, Haifeng; Liu, Zhe; Li, Jinshan

doi:10.1007/s12598-022-02152-5

Cited by 22 publications

(9 citation statements)

References 108 publications

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“…Considering the reliability in the EWF‐scaled mechanical properties have been proposed and validated in various materials 33,72 . The well‐fitted and explainable six power law 43 proposed in our previous work is used to validate the present targeted hardness of HEBs, which can be expressed as:

\begin{equation}{H}_{\mathrm{V}} = {H}_{{\mathrm{SS}}} + {H}_d = 0.0203\varphi _{{\mathrm{FP}}}^6 + 9.2064{d}^{ - 1/2} - 147.9839\end{equation}

…”

Section: Resultsmentioning

confidence: 99%

“…Here, d is the grain size and the EWF value φ FP is addressed by HDFE model 74 . Considering that a grain size of approximately 1.59 μm has been applied on power law scale 43 and fabricated HEBs with existing experimental processing methods, 8

\ d\ = \ 1.59\ {\mathrm{\mu m\ }}

is initially utilized to validate the ML predicted hardness of these 14 screened candidate HEBs.…”

Section: Resultsmentioning

confidence: 99%

“…[37][38][39] The designable bonding features (i.e., the deformable covalent bonds of SiC 40 ) could significantly improve the ductility of ceramics. 2,40 In line with the design strategy in terms of lattice distortion/engineering, 8,37-39,41 computational thermodynamics can be utilized to efficiently select, estimate, and validate the best candidates, 21,[42][43][44] in which the thermal conductivity can be decreased via disorder. 41,45 Moreover, the thermodynamic and physical descriptors have been combined in the development of a robust ML 46,47 model predicting the high-temperature yield strength of highentropy materials.…”

Section: Introductionmentioning

confidence: 99%

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Discovering superhard high‐entropy diboride ceramics via a hybrid data‐driven and knowledge‐enabled model

Zhang

Wang

et al. 2023

J Am Ceram Soc.

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Materials descriptors with multivariate, multiphase, and multiscale of a complex system have been treated as the remarkable materials genome, addressing the composition–processing–structure–property–performance (CPSPP) relationships during the development of advanced materials. With the aid of high‐performance computations, big data, and artificial intelligence technologies, it is still a challenge to derive an explainable machine learning (ML) model to reveal the underlying CPSPP relationship, especially, under the extreme conditions. This work supports a smart strategy to derive an explainable model of composition–property–performance relationships via a hybrid data‐driven and knowledge‐enabled model, and designing superhard high‐entropy diboride ceramics (HEBs) with a cost‐effective approach. Five dominate features and optimal model were screened out from 149 features and nine algorithms by ML and validated in first‐principles calculations. From Shapley additive explanations (SHAP) and electronic bottom layer, the predicted hardness increases with the improved mean electronegativity and electron work function (EWF) and decreases with growing average d valence electrons of composition. The 14 undeveloped potential superhard HEBs candidates via ML are further validated by first‐principles calculations. Moreover, this EWF‐ML model not only has better capability to distinguish the differences of solutes in same group of periodic table but is also a more effective method for material design than that of valence electron concentration.

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\begin{equation}{H}_{\mathrm{V}} = {H}_{{\mathrm{SS}}} + {H}_d = 0.0203\varphi _{{\mathrm{FP}}}^6 + 9.2064{d}^{ - 1/2} - 147.9839\end{equation}

…”

Section: Resultsmentioning

confidence: 99%

\ d\ = \ 1.59\ {\mathrm{\mu m\ }}

is initially utilized to validate the ML predicted hardness of these 14 screened candidate HEBs.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering superhard high‐entropy diboride ceramics via a hybrid data‐driven and knowledge‐enabled model

Zhang

Wang

et al. 2023

J Am Ceram Soc.

Self Cite

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show abstract

“…Considering the reliability in the EWF-scaled with mechanical properties have been proposed and validated in various materials 32,64 . The well-tted and explainable six power law proposed in our previous work is used to validate the present targeted hardness of HEBs 40 , which can be expressed as:…”

Section: Validations Smart Design Strategy Via Rst-principles Calcula...mentioning

confidence: 99%

“…In line with the designing strategy in term of lattice distortion/engineering 3,[34][35][36]38 , computational thermodynamics can be utilized to select and validate the best candidates e ciently 18,39,40 , in which the thermal conductivity can be decreased via disorder 38 . Moreover, the thermodynamic and physical descriptors have been combined in the development of a robust ML model predicting the high temperature yield strength of high-entropy materials 38 .…”

Section: Introductionmentioning

confidence: 99%

Revealing the Materials Genome of Superhard High-Entropy Diborides via the Hybrid Data-driven and Knowledge-enabled Model

Wang

Zhang

et al. 2023

Preprint

Self Cite

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Materials descriptors with multivariate, multiphase and multiscale of a complex system have been treated as the remarkable materials genome, addressing the composition-processing-structure-property-performance (CPSPP) relationships during the development of advanced materials. With the aid of high-performance computations, big data and artificial intelligent technologies, it is still a challenge to derive the explainable machine learned model to reveal the underlaying CPSPP relationship, especially, under the extreme conditions. Here, we propose a hybrid data-driven and knowledge-enabled model with two key descriptors to design the superhard high entropy boride ceramics (HEBs), which is not only in line with the common features from various machine learning algorithms but also integrate the solid-solution strengthening mechanisms. While five dominate features in terms of load, valence differences, electronegativity, electron work functions, and the differences among solutes in various column of periodical elementary table were screened out from 149 ones, the best optimal machine learning (ML) algorithm was addressed among decision tree, support vector regression, K-Nearest Neighbor, random forest, Adaboost, gradient enhanced regression tree, Bagging, ExtraTree, and XGBoost. The Shapley additive explanation the key influence trend for material hardness with the change of HEBs electronic properties. Correspondingly, the predicted 14 potential best superhard HEB candidates via ML are further validated by first-principles calculations via the aforementioned knowledge-based model. This work supports a smart strategy to derive the hybrid data-driven and knowledge-enable explainable model predicting the target properties of advanced HEBs and paves a path accelerating their development at cost-effective approach.

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Scalable Synthesis Methods for High‐Entropy Nanoparticles

Ritter,

Pappu,

Shahbazian‐Yassar

2024

Adv Energy and Sustain Res

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High‐entropy materials (HEMs) represent a revolutionary class of materials that have garnered significant attention in the field of materials science due to their extraordinary properties in diverse fields of applications such as catalysis and electrochemistry. The past decade has witnessed a substantial increase in the study of these materials, exploring new synthesis routes and compositions. What began as the synthesis of high‐entropy alloys has expanded to encompass several classes of HEMs such as oxides, hydroxides, sulfides, nitrides, and carbides, among others. Several synthesis methods have been developed to produce these materials. This review therefore highlights the fundamental concepts of HEMs, including their core effects, with a major emphasis on their scalable synthesis routes. The advantages and drawbacks of these methods are also discussed. As HEMs transition from the lab to large‐scale production, there is a growing need for cost‐effective and scalable synthesis methods with high material yield suitable for a variety of applications like hydrogen storage, catalysis, batteries, supercapacitors, and fuel cells. Hence, this review serves as an introduction to scalable synthesis routes based on crystal structure, desired elements, synthesis times, and equipment costs.

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Electronic structures and strengthening mechanisms of superhard high-entropy diborides

Cited by 22 publications

References 108 publications

Discovering superhard high‐entropy diboride ceramics via a hybrid data‐driven and knowledge‐enabled model

Discovering superhard high‐entropy diboride ceramics via a hybrid data‐driven and knowledge‐enabled model

Revealing the Materials Genome of Superhard High-Entropy Diborides via the Hybrid Data-driven and Knowledge-enabled Model

Scalable Synthesis Methods for High‐Entropy Nanoparticles

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