Rational design of high-entropy ceramics based on machine learning – A critical review
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Yaoxu Xiong | Shihua Ma | Shasha Huang | Hongyu Chen | Yining Ma | Biao Xu | Jun Zhang | Shijun Zhao | Zhenggang Wu | Xuepeng Xiang | Haijun Fu
[1] T. Ishihara,et al. Significant CO2 photoreduction on a high-entropy oxynitride , 2022, Chemical Engineering Journal.
[2] M. Reece,et al. Machine learning of carbon vacancy formation energy in high-entropy carbides , 2022, Journal of the European Ceramic Society.
[3] Dan Lu,et al. Overview: recent studies of machine learning in phase prediction of high entropy alloys , 2022, Tungsten.
[4] Jiaxin Zhang,et al. Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities , 2022, Progress in Materials Science.
[5] Jack K. Pedersen,et al. Exploring the Composition Space of High-Entropy Alloy Nanoparticles for the Electrocatalytic H2/CO Oxidation with Bayesian Optimization , 2022, ACS Catalysis.
[6] M. Wagih,et al. Learning Grain-Boundary Segregation: From First Principles to Polycrystals. , 2022, Physical review letters.
[7] Z. Chen,et al. High Hydrogen Isotopes Permeation Resistance in (TiVAlCrZr)O Multi-component Metal Oxide Glass Coating , 2022, Acta Materialia.
[8] Xiaopeng Han,et al. Electrical Discharge Induced Bulk‐to‐Nanoparticle Transformation: Nano High‐Entropy Carbide as Catalysts for Hydrogen Evolution Reaction , 2022, Advanced Functional Materials.
[9] Shijun Zhao. Defect energetics and stacking fault formation in high-entropy carbide ceramics , 2022, Journal of the European Ceramic Society.
[10] L. Luo,et al. Xe-ion-irradiation-induced structural transitions and elemental diffusion in high-entropy alloy and nitride thin-film multilayers , 2022, Materials & Design.
[11] Alice E. A. Allen,et al. Machine learning of material properties: Predictive and interpretable multilinear models , 2022, Science advances.
[12] Zheng-qiu Wu,et al. Toughening (NbTaZrW)C high entropy carbide ceramic through Mo doping , 2022, Journal of the American Ceramic Society.
[13] Huadong Fu,et al. Recent progress in the machine learning-assisted rational design of alloys , 2022, International Journal of Minerals, Metallurgy and Materials.
[14] Prashant Singh,et al. Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys , 2022, Acta Materialia.
[15] D. Morgan,et al. Machine learning in nuclear materials research , 2022, Current Opinion in Solid State and Materials Science.
[16] B. Goldsmith,et al. Interpretable machine learning for knowledge generation in heterogeneous catalysis , 2022, Nature Catalysis.
[17] Wenjie Zhu,et al. Phase formation prediction of high-entropy alloys: A deep learning study , 2022, Journal of Materials Research and Technology.
[18] K. Biswas,et al. Machine learning based approach for phase prediction in high entropy borides , 2022, Ceramics International.
[19] Yaoxu Xiong,et al. Design high-entropy carbide ceramics from machine learning , 2022, npj Computational Materials.
[20] P. Liaw,et al. Machine-learning and high-throughput studies for high-entropy materials , 2022, Materials Science and Engineering: R: Reports.
[21] K. Hamad,et al. Machine learning guided discovery of super-hard high entropy ceramics , 2022, Materials Letters.
[22] Shijun Zhao. Application of machine learning in understanding the irradiation damage mechanism of high-entropy materials , 2021, Journal of Nuclear Materials.
[23] Yanwen Zhang,et al. Sluggish, chemical bias and percolation phenomena in atomic transport by vacancy and interstitial diffusion in Ni Fe alloys , 2021, Current Opinion in Solid State and Materials Science.
[24] R. Arróyave,et al. Towards Stacking Fault Energy Engineering in FCC High Entropy Alloys , 2021, Acta Materialia.
[25] Yunqing Tang,et al. Designing high-entropy ceramics via incorporation of the bond-mechanical behavior correlation with the machine-learning methodology , 2021, Cell Reports Physical Science.
[26] Ling Qiao,et al. A focused review on machine learning aided high-throughput methods in high entropy alloy , 2021 .
[27] K. Edalati,et al. High-entropy ceramics: Review of principles, production and applications , 2021, Materials Science and Engineering: R: Reports.
[28] M. Gaultois,et al. Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry , 2021, Nature Communications.
[29] K. Vecchio,et al. Development of ultrahigh-entropy ceramics with tailored oxidation behavior , 2021 .
[30] Jinjin Li,et al. Unsupervised discovery of thin-film photovoltaic materials from unlabeled data , 2021, npj Computational Materials.
[31] S. Curtarolo,et al. Machine learning for alloys , 2021, Nature Reviews Materials.
[32] Gaurav Goel,et al. Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications , 2021, Emergent Materials.
[33] Yuguang C. Li,et al. A novel (La0.2Ce0.2Gd0.2Er0.2Tm0.2)2(WO4)3 high-entropy ceramic material for thermal neutron and gamma-ray shielding , 2021, Materials & Design.
[34] T. Ishihara,et al. High-entropy oxynitride as a low-bandgap and stable photocatalyst for hydrogen production , 2021 .
[35] S. Curtarolo,et al. Carbon stoichiometry and mechanical properties of high entropy carbides , 2021 .
[36] Brian L. DeCost,et al. Atomistic Line Graph Neural Network for improved materials property predictions , 2021, npj Computational Materials.
[37] Jonathan P. Mailoa,et al. Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture , 2021, npj Computational Materials.
[38] Shijun Zhao,et al. High-entropy carbide ceramics: a perspective review , 2021, Tungsten.
[39] Yanchun Zhou,et al. Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential , 2021 .
[40] D. Brenner,et al. High-Entropy Ultra-High-Temperature Borides and Carbides: A New Class of Materials for Extreme Environments , 2021 .
[41] Chi Chen,et al. Accelerating materials discovery with Bayesian optimization and graph deep learning , 2021, Materials Today.
[42] Yanchun Zhou,et al. Application of high-throughput first-principles calculations in ceramic innovation , 2021 .
[43] Jianxin Xie,et al. Towards high-throughput microstructure simulation in compositionally complex alloys via machine learning , 2021 .
[44] Shijun Zhao,et al. Phase, microstructure and related mechanical properties of a series of (NbTaZr)C-Based high entropy ceramics , 2021 .
[45] Changfeng Chen,et al. Sorting transition-metal diborides: New descriptor for mechanical properties , 2021 .
[46] Zachary W. Ulissi,et al. Open Catalyst 2020 (OC20) Dataset and Community Challenges , 2020, ACS Catalysis.
[47] Tonio Buonassisi,et al. An Invertible Crystallographic Representation for General Inverse Design of Inorganic Crystals with Targeted Properties , 2020, SSRN Electronic Journal.
[48] Pau Cutrina Vilalta,et al. Machine Learning for Predicting the Critical Yield Stress of High Entropy Alloys , 2021 .
[49] Shijun Zhao. Lattice distortion in high‐entropy carbide ceramics from first‐principles calculations , 2020 .
[50] E. Olivetti,et al. Data-driven materials research enabled by natural language processing and information extraction , 2020, Applied Physics Reviews.
[51] Tyler J. Harrington,et al. Bulk high-entropy nitrides and carbonitrides , 2020, Scientific Reports.
[52] A. Mukasyan,et al. Extremely hard and tough high entropy nitride ceramics , 2020, Scientific Reports.
[53] T. Wen,et al. Thermophysical and mechanical properties of novel high‐entropy metal nitride‐carbides , 2020 .
[54] G. Ceder,et al. Cation-disordered rocksalt-type high-entropy cathodes for Li-ion batteries , 2020, Nature Materials.
[55] M. Nastasi,et al. Irradiation damage in (Zr0.25Ta0.25Nb0.25Ti0.25)C high-entropy carbide ceramics , 2020 .
[56] D. Morgan,et al. Opportunities and Challenges for Machine Learning in Materials Science , 2020, Annual Review of Materials Research.
[57] Pooyan Jamshidi,et al. High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery. , 2020, Physical chemistry chemical physics : PCCP.
[58] B. Dunn,et al. A general method to synthesize and sinter bulk ceramics in seconds , 2020, Science.
[59] Alexander S. Rosengarten,et al. Discovery of high-entropy ceramics via machine learning , 2020, npj Computational Materials.
[60] Yanchun Zhou,et al. Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential , 2020 .
[61] S. Curtarolo,et al. High-entropy ceramics , 2020, Nature Reviews Materials.
[62] Zhiqian Chen,et al. Unsupervised discovery of solid-state lithium ion conductors , 2019, Nature Communications.
[63] M. Reece,et al. Review of high entropy ceramics: design, synthesis, structure and properties , 2019, Journal of Materials Chemistry A.
[64] M. Marques,et al. Recent advances and applications of machine learning in solid-state materials science , 2019, npj Computational Materials.
[65] Dierk Raabe,et al. High-entropy alloys , 2019, Nature Reviews Materials.
[66] Turab Lookman,et al. Machine learning assisted design of high entropy alloys with desired property , 2019, Acta Materialia.
[67] H. Zhuang,et al. Machine-learning phase prediction of high-entropy alloys , 2019, Acta Materialia.
[68] Krishna Rajan,et al. New frontiers for the materials genome initiative , 2019, npj Computational Materials.
[69] Elizabeth A. Holm,et al. In defense of the black box , 2019, Science.
[70] Tyler J. Harrington,et al. Phase stability and mechanical properties of novel high entropy transition metal carbides , 2019, Acta Materialia.
[71] Turab Lookman,et al. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design , 2019, npj Computational Materials.
[72] Chi Chen,et al. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals , 2018, Chemistry of Materials.
[73] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[74] Chenglong Zhao,et al. High-entropy chemistry stabilizing layered O3-type structure in Na-ion cathode. , 2019, Angewandte Chemie.
[75] Jinyong Zhang,et al. High-entropy carbide: A novel class of multicomponent ceramics , 2018, Ceramics International.
[76] Cormac Toher,et al. High-entropy high-hardness metal carbides discovered by entropy descriptors , 2018, Nature Communications.
[77] Zachary W. Ulissi,et al. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution , 2018, Nature Catalysis.
[78] Qingsong Wang,et al. High entropy oxides for reversible energy storage , 2018, Nature Communications.
[79] S. Grasso,et al. Processing and Properties of High-Entropy Ultra-High Temperature Carbides , 2018, Scientific Reports.
[80] D. Gall,et al. Valence electron concentration as an indicator for mechanical properties in rocksalt structure nitrides, carbides and carbonitrides , 2018, Acta Materialia.
[81] Jun Hu,et al. Mechanochemical‐Assisted Synthesis of High‐Entropy Metal Nitride via a Soft Urea Strategy , 2018, Advanced materials.
[82] Michael F. Ashby,et al. Materials selection for nuclear applications: Challenges and opportunities , 2018 .
[83] Ole Winther,et al. Deep Generative Models for Molecular Science , 2018, Molecular informatics.
[84] E Weinan,et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics , 2017, Comput. Phys. Commun..
[85] Cormac Toher,et al. The search for high entropy alloys: A high-throughput ab-initio approach , 2017, Acta Materialia.
[86] Jeffrey C Grossman,et al. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. , 2017, Physical review letters.
[87] E Weinan,et al. Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics , 2017, Physical review letters.
[88] P. Liaw,et al. First-principles prediction of high-entropy-alloy stability , 2017, npj Computational Materials.
[89] Tyler J. Harrington,et al. High-Entropy Metal Diborides: A New Class of High-Entropy Materials and a New Type of Ultrahigh Temperature Ceramics , 2016, Scientific Reports.
[90] D. Miracle,et al. A critical review of high entropy alloys and related concepts , 2016 .
[91] S. Franger,et al. Room temperature lithium superionic conductivity in high entropy oxides , 2016 .
[92] Muratahan Aykol,et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies , 2015 .
[93] Jacob L. Jones,et al. Entropy-stabilized oxides , 2015, Nature Communications.
[94] C. Weinberger,et al. Bonding effects on the slip differences in the B1 monocarbides. , 2015, Physical review letters.
[95] J. Pablo,et al. The Materials Genome Initiative, the interplay of experiment, theory and computation , 2014 .
[96] B. Uberuaga,et al. Defect behavior in oxides: Insights from modern atomistic simulation methods , 2013 .
[97] Muratahan Aykol,et al. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) , 2013 .
[98] Kristin A. Persson,et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .
[99] H. Bei,et al. Relative effects of enthalpy and entropy on the phase stability of equiatomic high-entropy alloys , 2013 .
[100] Steven J. Zinkle,et al. Materials Challenges in Nuclear Energy , 2013 .
[101] Marco Buongiorno Nardelli,et al. AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations , 2012 .
[102] C. Liu,et al. Phase stability in high entropy alloys: Formation of solid-solution phase or amorphous phase , 2011 .
[103] C. Liu,et al. Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys , 2011 .
[104] P. Liaw,et al. Solid‐Solution Phase Formation Rules for Multi‐component Alloys , 2008 .
[105] J. Yeh. Recent progress in high-entropy alloys , 2006 .
[106] Jien-Wei Yeh,et al. Nanostructured nitride films of multi-element high-entropy alloys by reactive DC sputtering , 2004 .
[107] B. Cantor,et al. Microstructural development in equiatomic multicomponent alloys , 2004 .
[108] T. Shun,et al. Nanostructured High‐Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and Outcomes , 2004 .
[109] S. Louie,et al. Electronic mechanism of hardness enhancement in transition-metal carbonitrides , 1998, Nature.