4.7 Article

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

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Materials Science, Multidisciplinary

Electronic structures and strengthening mechanisms of superhard high-entropy diborides

Gang Yao et al.

Summary: This study comprehensively investigates the effects of transition metals on high-entropy diborides (HEBs) and reveals the optimization mechanism of hardness based on the lattice distortion and electron contribution of transition metal atoms.

RARE METALS (2023)

Article Materials Science, Multidisciplinary

Machine learning versus human learning in predicting glass-forming ability of metallic glasses

Guannan Liu et al.

Summary: Machine learning shows potential in addressing complex materials science problems, but physical insights are necessary to develop accurate and predictable models.

ACTA MATERIALIA (2023)

Article Materials Science, Multidisciplinary

A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness

Chen Yang et al.

Summary: Efficiently discovering novel high entropy alloys (HEAs) with exceptional performance remains a great challenge due to traditional trial-and-error methods. A machine learning-based alloy design system (MADS) was introduced to rationalize the design of HEAs with enhanced hardness. By constructing a hardness database and utilizing feature selection, a hardness prediction model based on support vector machine was established, which led to the synthesis of optimized compositions with ultra-high hardness. Importantly, the model interpretability was enhanced with the introduction of the Shapley additive explanation (SHAP) method.

ACTA MATERIALIA (2022)

Article Materials Science, Ceramics

Local orders, lattice distortions, and electronic structure dominated mechanical properties of (ZrHfTaM1M2)C (M = Nb, Ti, V)

Gang Yao et al.

Summary: In this study, the effects of transition metals on high-entropy carbides (HECs) were comprehensively investigated using first-principles calculations. The results showed that the introduction of transition metals can improve the lattice parameters and bulk modulus of HECs. Additionally, the proposed power-law-scaled hardness of HECs was validated using electron work function (EWF) analysis, providing a strategy to design advanced HECs with excellent mechanical properties.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2022)

Article Materials Science, Ceramics

Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations

Mohd Zaki et al.

Summary: This paper demonstrates the use of machine learning models to predict and interpret the optical properties of oxide glasses, showing the application of ML models in compositional control and providing explanations of the model results through Shapely additive explanations (SHAP).

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2022)

Article Chemistry, Physical

Design high-entropy carbide ceramics from machine learning

Jun Zhang et al.

Summary: In this study, machine-learning models were developed to discover high-entropy ceramic carbides (HECCs). These models showed high prediction accuracy and were used to evaluate the single-phase probability of 90 HECCs. Some of these predictions were validated through experiments. Phase diagrams for non-equiatomic HECCs were also established, enabling easy identification of single-phase regions. This research paves the way for the high-throughput design of high-performance HECCs based on chemical descriptors of constituent transition-metal-carbide precursors.

NPJ COMPUTATIONAL MATERIALS (2022)

Article Materials Science, Multidisciplinary

High-entropy ceramics: Propelling applications through disorder

Cormac Toher et al.

Summary: Disorder enhances desired material properties and provides new approaches for material synthesis. High-entropy ceramics have wide-ranging applications in various fields, such as coatings, thermal and environmental barriers, catalysts, batteries, thermoelectrics, and nuclear energy management.

MRS BULLETIN (2022)

Article Materials Science, Ceramics

Predicting mechanical properties of ultrahigh temperature ceramics using machine learning

Taihao Han et al.

Summary: This study uses machine learning models to predict the mechanical properties of ultrahigh temperature ceramics and develops a simplified prediction model based on the ranking of input variables. The results show that data-driven numerical models can accelerate the development of ultrahigh temperature ceramics.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2022)

Article Chemistry, Physical

Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength

Stephen A. Giles et al.

Summary: This study demonstrates a method of intelligently exploring the compositional space of refractory high-entropy alloys (RHEAs) through a machine learning framework and optimization methods, successfully discovering RHEA alloys with superior high-temperature yield strengths.

NPJ COMPUTATIONAL MATERIALS (2022)

Article Multidisciplinary Sciences

Low-hysteresis shape-memory ceramics designed by multimode modelling

Edward L. Pang et al.

Summary: This study presents an improved lattice engineering method to enhance the martensitic properties of zirconia ceramics. By utilizing modern computational thermodynamics and data science tools, a new zirconia composition with significantly reduced hysteresis is obtained. The findings indicate that zirconia ceramics can exhibit hysteresis values comparable to widely used shape-memory alloys, making them potential high-temperature shape-memory materials.

NATURE (2022)

Article Materials Science, Multidisciplinary

Integrating data mining and machine learning to discover high-strength ductile titanium alloys

Chengxiong Zou et al.

Summary: The article showcases a new approach to materials engineering design and optimization, using high-throughput calculations and machine learning methods with titanium alloys as an example to improve their strength and ductility. The integration of data mining and machine learning has been shown to result in the more efficient and cost-effective design of strong and ductile titanium alloys.

ACTA MATERIALIA (2021)

Article Materials Science, Ceramics

A new class of high-entropy M3B4 borides

Mingde Qin et al.

Summary: A new class of high-entropy M3B4 borides with Ta3B4-prototyped orthorhombic structure has been synthesized in bulk form. The specimens were fabricated through reactive spark plasma sintering and showed high Vickers hardness values, expanding the family of reported high-entropy ceramics with different structures.

JOURNAL OF ADVANCED CERAMICS (2021)

Article Chemistry, Physical

Recent progress in high-entropy alloys for catalysts: synthesis, applications, and prospects

K. Li et al.

Summary: High-entropy alloys (HEAs) have attracted great interest in the field of electro/thermo-catalytic clean energy conversion due to their unique characteristics. Research on the synthesis and catalytic applications of HEAs is ongoing, with computationally aided methods playing a crucial role in their design and discovery.

MATERIALS TODAY ENERGY (2021)

Article Materials Science, Multidisciplinary

Theory of solid solution strengthening of BCC Chemically Complex Alloys

S. I. Rao et al.

Summary: An analytic model for substitutional solid solution strengthening in BCC CCAs based on a/2<111> screw dislocation mobility is developed and presented. Different strengthening mechanisms are observed at different temperatures, requiring modifications to existing models to apply to refractory CCAs.

ACTA MATERIALIA (2021)

Review Materials Science, Multidisciplinary

High-entropy ceramics: Review of principles, production and applications

Saeid Akrami et al.

Summary: High-entropy ceramics, containing five or more cations, have attracted significant attention recently for their superior properties in various structural and functional applications. Efforts have been made to increase entropy, minimize Gibbs free energy and achieve stable single-phase high-entropy ceramics. These materials have potential for future applications in fields such as oxides, nitrides, carbides, borides, and hydrides.

MATERIALS SCIENCE & ENGINEERING R-REPORTS (2021)

Article Materials Science, Multidisciplinary

Physics-informed machine learning for composition - process - property design: Shape memory alloy demonstration

Sen Liu et al.

Summary: Machine learning is applied to predict new alloys and performance, combining elemental and heat treatment features to improve model performance using physics-based methods, validating predictive design capabilities.

APPLIED MATERIALS TODAY (2021)

Article Materials Science, Multidisciplinary

Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential

Fu-Zhi Dai et al.

Summary: The study predicted the thermal and elastic properties of high entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B-2 at different temperatures, and found that the predicted values agree well with experimental measurements. The use of machine learning potential opens up new possibilities for gaining insights into high entropy materials.

JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY (2021)

Review Materials Science, Ceramics

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

Huimin Xiang et al.

Summary: High-entropy ceramics are solid solutions of inorganic compounds with diverse crystal and electronic structures, providing large space for property tuning through band structure and phonon engineering. In addition to traditional strengthening, hardening, and low thermal conductivity, HECs exhibit new properties such as colossal dielectric constant and super ionic conductivity. Challenges in processing, characterization, and property predictions are highlighted, along with future directions for material exploration and in-depth characterization.

JOURNAL OF ADVANCED CERAMICS (2021)

Review Materials Science, Multidisciplinary

Accelerating materials discovery using machine learning

Yongfei Juan et al.

Summary: The use of machine learning methods in materials science has seen significant progress in recent years, bringing about a profound revolution in society and greatly advancing scientific development. This review provides an overview of the application of machine learning in materials science research, emphasizing the main ideas, basic procedures, and classification and comparison of commonly used algorithms.

JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY (2021)

Review Chemistry, Physical

Electronic-structure methods for materials design

Nicola Marzari et al.

Summary: Simulations, using electronic-structure methods such as density functional theory, have driven a new paradigm in research by accelerating the identification, characterization, and optimization of materials. The accuracy and efficiency of these methods rely on the predictive accuracy of underlying physical descriptions and the ability to capture system complexity. Continuous progress in theory, algorithms, and hardware, along with the adaptation of tools from computer science, play a key role in advancing materials science.

NATURE MATERIALS (2021)

Article Materials Science, Ceramics

Enabling highly efficient and broadband electromagnetic wave absorption by tuning impedance match in high-entropy transition metal diborides (HE TMB2)

Weiming Zhang et al.

Summary: The rapid advancement in communication technology has raised global concern regarding electromagnetic wave pollution. To address this issue, the development of high-performance electromagnetic wave absorbing materials with coupled dielectric and magnetic losses is urgently needed. Through high-entropy engineering, three different powders of transition metal diborides were designed and prepared, showcasing improved absorption performance and impedance match.

JOURNAL OF ADVANCED CERAMICS (2021)

Review Nanoscience & Nanotechnology

Machine learning for alloys

Gus L. W. Hart et al.

Summary: The integration of machine learning and alloys has played a crucial role in advancing research on various materials. Computational materials science has benefited from advancements in machine learning methods and data generation, opening up new possibilities for alloy research.

NATURE REVIEWS MATERIALS (2021)

Article Materials Science, Ceramics

Formation criterion for binary metal diboride solid solutions established through combinatorial methods

Tongqi Wen et al.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2020)

Review Nanoscience & Nanotechnology

High-entropy ceramics

Corey Oses et al.

NATURE REVIEWS MATERIALS (2020)

Review Materials Science, Multidisciplinary

A step forward from high-entropy ceramics to compositionally complex ceramics: a new perspective

Andrew J. Wright et al.

JOURNAL OF MATERIALS SCIENCE (2020)

Article Materials Science, Ceramics

The effect of submicron grain size on thermal stability and mechanical properties of high-entropy carbide ceramics

Fei Wang et al.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2020)

Article Materials Science, Ceramics

Density functional theory and machine learning guided search for RE2Si2O7 with targeted coefficient of thermal expansion

Mukil V. Ayyasamy et al.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2020)

Article Chemistry, Physical

Discovery of high-entropy ceramics via machine learning

Kevin Kaufmann et al.

NPJ COMPUTATIONAL MATERIALS (2020)

Article Materials Science, Multidisciplinary

Searching for high entropy alloys: A machine learning approach

Kevin Kaufmann et al.

ACTA MATERIALIA (2020)

Article Materials Science, Ceramics

A novel high-entropy monoboride (Mo0.2Ta0.2Ni0.2Cr0.2W0.2)B with superhardness and low thermal conductivity

Pengbo Zhao et al.

CERAMICS INTERNATIONAL (2020)

Article Chemistry, Physical

A structural modeling approach to solid solutions based on the similar atomic environment

Fuyang Tian et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Review Materials Science, Multidisciplinary

Review on ultra-high temperature boride ceramics

Brahma Raju Golla et al.

PROGRESS IN MATERIALS SCIENCE (2020)

Article Chemistry, Physical

Coupling physics in machine learning to predict properties of high-temperatures alloys

Jian Peng et al.

NPJ COMPUTATIONAL MATERIALS (2020)

Article Nanoscience & Nanotechnology

High-entropy monoborides: Towards superhard materials

Mingde Qin et al.

SCRIPTA MATERIALIA (2020)

Article Materials Science, Multidisciplinary

Thermal stability and mechanical properties of sputtered (Hf,Ta,V,W,Zr)-diborides

A. Kirnbauer et al.

ACTA MATERIALIA (2020)

Article Multidisciplinary Sciences

Enhanced Hardness in High-Entropy Carbides through Atomic Randomness

Yichen Wang et al.

ADVANCED THEORY AND SIMULATIONS (2020)

Review Materials Science, Multidisciplinary

Integrated computational materials engineering for advanced materials: A brief review

William Yi Wang et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

Article Nanoscience & Nanotechnology

Dense high-entropy boride ceramics with ultra-high hardness

Yan Zhang et al.

SCRIPTA MATERIALIA (2019)

Article Materials Science, Ceramics

Data-driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Eileen De Guire et al.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2019)

Article Materials Science, Multidisciplinary

Computation of entropies and phase equilibria in refractory V-Nb-Mo-Ta-W high-entropy alloys

Yi Wang et al.

ACTA MATERIALIA (2018)

Article Materials Science, Multidisciplinary

Revealing the local lattice strains and strengthening mechanisms of Ti alloys

Chengxiong Zou et al.

COMPUTATIONAL MATERIALS SCIENCE (2018)

Article Chemistry, Physical

Revealing the Microstates of Body-Centered-Cubic (BCC) Equiatomic High Entropy Alloys

William Yi Wang et al.

JOURNAL OF PHASE EQUILIBRIA AND DIFFUSION (2017)

Article Nanoscience & Nanotechnology

Ultra-high temperature ceramics: Materials for extreme environments

William G. Fahrenholtz et al.

SCRIPTA MATERIALIA (2017)

Article Multidisciplinary Sciences

Ablation-resistant carbide Zr0.8Ti0.2C0.74B0.26 for oxidizing environments up to 3,000 °C

Yi Zeng et al.

NATURE COMMUNICATIONS (2017)

Review Nanoscience & Nanotechnology

Functional carbon nitride materials design strategies for electrochemical devices

Fabian K. Kessler et al.

NATURE REVIEWS MATERIALS (2017)

Article Chemistry, Physical

Atomic and electronic basis for the serrations of refractory high-entropy alloys

William Yi Wang et al.

NPJ COMPUTATIONAL MATERIALS (2017)

Article Materials Science, Multidisciplinary

Local lattice distortion in high-entropy alloys

Hongquan Song et al.

PHYSICAL REVIEW MATERIALS (2017)

Review Materials Science, Multidisciplinary

A critical review of high entropy alloys and related concepts

D. B. Miracle et al.

ACTA MATERIALIA (2017)

News Item Chemistry, Physical

The frontiers and the challenges

Nicola Marzari

NATURE MATERIALS (2016)

Article Physics, Multidisciplinary

Efficient Ab initio Modeling of Random Multicomponent Alloys

Chao Jiang et al.

PHYSICAL REVIEW LETTERS (2016)

Article Nanoscience & Nanotechnology

Power law scaled hardness of Mn strengthened nanocrystalline Al-Mn non-equilibrium solid solutions

William Yi Wang et al.

SCRIPTA MATERIALIA (2016)

Article Chemistry, Physical

A general-purpose machine learning framework for predicting properties of inorganic materials

Logan Ward et al.

NPJ COMPUTATIONAL MATERIALS (2016)

Review Materials Science, Multidisciplinary

Materials Informatics: The Materials Gene and Big Data

Krishna Rajan

ANNUAL REVIEW OF MATERIALS RESEARCH, VOL 45 (2015)

Article Chemistry, Multidisciplinary

Bonding Charge Density from Atomic Perturbations

Yi Wang et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2015)

Article Physics, Multidisciplinary

Big Data of Materials Science: Critical Role of the Descriptor

Luca M. Ghiringhelli et al.

PHYSICAL REVIEW LETTERS (2015)

Review Materials Science, Multidisciplinary

Microstructures and properties of high-entropy alloys

Yong Zhang et al.

PROGRESS IN MATERIALS SCIENCE (2014)

Review Materials Science, Multidisciplinary

High-Entropy Alloys: A Critical Review

Ming-Hung Tsai et al.

MATERIALS RESEARCH LETTERS (2014)

Article Physics, Condensed Matter

The correlation between the electron work function and yield strength of metals

Guomin Hua et al.

PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS (2012)

Article Physics, Applied

Generic relation between the electron work function and Young's modulus of metals

Guomin Hua et al.

APPLIED PHYSICS LETTERS (2011)

Article Chemistry, Physical

Modeling hardness of polycrystalline materials and bulk metallic glasses

Xing-Qiu Chen et al.

INTERMETALLICS (2011)

Article Materials Science, Multidisciplinary

Microscopic Models of Hardness

F. M. Gao et al.

JOURNAL OF SUPERHARD MATERIALS (2010)

Article Physics, Multidisciplinary

Prediction of Glass Hardness Using Temperature-Dependent Constraint Theory

Morten M. Smedskjaer et al.

PHYSICAL REVIEW LETTERS (2010)

Article Multidisciplinary Sciences

Hardness of materials: studies at levels from atoms to crystals

Li KeYan et al.

CHINESE SCIENCE BULLETIN (2009)

Article Physics, Multidisciplinary

Thermodynamic aspects of materials' hardness: prediction of novel superhard high-pressure phases

V. A. Mukhanov et al.

HIGH PRESSURE RESEARCH (2008)

Article Physics, Multidisciplinary

Electronegativity identification of novel superhard materials

Keyan Li et al.

PHYSICAL REVIEW LETTERS (2008)

Article Physics, Multidisciplinary

Hardness of covalent and ionic crystals:: First-principle calculations

A Simunek et al.

PHYSICAL REVIEW LETTERS (2006)

Article Physics, Multidisciplinary

Hardness of covalent crystals

FM Gao et al.

PHYSICAL REVIEW LETTERS (2003)

Article Physics, Multidisciplinary

Combined electronic structure and evolutionary search approach to materials design -: art. no. 255506

GH Jóhannesson et al.

PHYSICAL REVIEW LETTERS (2002)