4.7 Article

Explainable predictions of multi-component oxides enabled by attention-based neural networks

Related references

Note: Only part of the references are listed.
Article Materials Science, Ceramics

Predicting oxidation damage of ultra high-temperature carbide ceramics in extreme environments using machine learning

Giuseppe Bianco et al.

Summary: By utilizing machine learning models trained with experimental data, we are able to predict the oxide thickness of UHTC carbides exposed to air based on various factors such as composition, grain size, densification, holding time, and temperature. The trained model successfully predicts the oxidation damage in UHTC carbides with a Mean Absolute Error of +/- 65.45 μm for samples up to 1000 μm in thickness.

CERAMICS INTERNATIONAL (2023)

Article Materials Science, Ceramics

Evolution behaviour of the lattice and thermal expansion of a high-entropy fluorite oxide (Zr0.2Ce0.2Hf0.2Y0.2Al0.2)O2-8 during heating and cooling in an inert atmosphere

Yubin Wen et al.

Summary: High-entropy fluorite oxides (HEFOs) are promising thermal barrier coatings (TBCs) with improved performance. The thermal expansion coefficient (TEC) plays a crucial role in the direct contact between TBCs and metal substrates. This study investigates the evolutionary behavior of lattice and thermal expansion of a specific HEFO (Zr0.2Ce0.2Hf0.2Y0.2Al0.2)O2-8 during heating and cooling in an inert atmosphere.

CERAMICS INTERNATIONAL (2023)

Article Nanoscience & Nanotechnology

Can unsupervised machine learning boost the on-site analysis of in situ synchrotron diffraction data?

T. Strohmann et al.

Summary: In this study, unsupervised machine learning is used to analyze in situ diffraction data of an additively manufactured Ti-6Al-4V alloy. The model is trained on a dataset consisting of four thermal cycles and can detect periods of fast phase transformation based on the steep gradient of the reconstruction error. Additionally, the features in the latent space of the autoencoder show correlation with the volume fractions of alpha/alpha' and beta. This methodology allows for on-site monitoring of phase transformation kinetics during experiments at synchrotrons without the need for continuous training or manual data labeling.

SCRIPTA MATERIALIA (2023)

Article Materials Science, Ceramics

Data-driven optimization of hardness and toughness of high-entropy nitride coatings

Shaoyu Wu et al.

Summary: In this paper, high-entropy nitride coatings with optimal hardness and elastic modulus combination were successfully obtained using a new material system combined with multi-objective optimization. The effects of elemental content on mechanical properties prediction in this system were visualized using partial dependence heatmaps, which helped to interpret the optimization results and discover unknown mapping relationships.

CERAMICS INTERNATIONAL (2023)

Article Materials Science, Ceramics

Thermophysical performances of high-entropy (La0.2Nd0.2Yb0.2Y0.2Sm0.2)2Ce2O7 and (La0.2Nd0.2Yb0.2Y0.2Lu0.2)2Ce2O7 oxides

An Tang et al.

Summary: Two high-entropy ceramics, LNYSC and LNYLC, were synthesized using a sol-gel technique and sintering at high temperatures. These oxides have single fluorite lattice, dense microstructure, and clear grain boundary. While exhibiting less thermal conductivities and larger thermal expansion coefficients than 7YSZ, these oxides also show outstanding lattice stability at high temperatures. However, their mechanical properties are lower compared to 7YSZ.

CERAMICS INTERNATIONAL (2022)

Article Materials Science, Multidisciplinary

A machine learning approach to predict thermal expansion of complex oxides

Jian Peng et al.

Summary: In this article, a machine learning approach for predicting the thermal expansion of oxides is presented. The approach utilizes experimental data and specific descriptors to train accurate models. The limitations of the current approach and challenges for future research are also discussed.

COMPUTATIONAL MATERIALS SCIENCE (2022)

Article Materials Science, Multidisciplinary

High-entropy (Y0.2 Gd0.2 Dy0.2 Er0.2 Yb0.2 )2Hf2O7 ceramic: A promising thermal barrier coating material

Longkang Cong et al.

Summary: In this study, a novel high entropy hafnate (Y0.2Gd0.2Dy0.2Er0.2Yb0.2)(2)Hf2O7 was synthesized and characterized for potential use as a thermal barrier coating (TBC) material in gas turbines. The (Y0.2Gd0.2Dy0.2Er0.2Yb0.2)(2)Hf2O7 demonstrated excellent phase stability, chemical compatibility with Al2O3, and lower thermal conductivity compared to other TBC materials. These results suggest that (Y0.2Gd0.2Dy0.2Er0.2Yb0.2)(2)Hf2O7 is a promising candidate for the next generation TBC materials.

JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY (2022)

Article Materials Science, Ceramics

Tuning stoichiometry of high-entropy oxides for tailorable thermal expansion coefficients and low thermal conductivity

Liang Xu et al.

Summary: The new high-entropy (La0.2Sm0.2Er0.2Yb0.2Y0.2)(2)CexO3+2x materials exhibit adjustable thermal expansion coefficient and engineered low thermal conductivity, making them promising candidates for thermal barrier coating materials, thermally insulating materials, and refractories.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2022)

Article Materials Science, Ceramics

High-entropy fluorite oxides: Atomic stabiliser effects on thermal-mechanical properties

Siao Li Liew et al.

Summary: High-entropy oxides with stable microstructures have been fabricated for potential applications in thermal barrier coatings, offering improved thermal expansion matching and thermal conductivity.

JOURNAL OF THE EUROPEAN CERAMIC SOCIETY (2022)

Article Nanoscience & Nanotechnology

A Universal Machine Learning Model for Elemental Grain Boundary Energies

Weike Ye et al.

Summary: The grain boundary energy significantly influences the grain growth and properties of polycrystalline metals. A machine learning model has been developed to predict the grain boundary energies with high accuracy and extrapolate to high sigma GB energies.

SCRIPTA MATERIALIA (2022)

Article Materials Science, Ceramics

Sand corrosion, thermal expansion, and ablation of medium- and high-entropy compositionally complex fluorite oxides

Andrew J. Wright et al.

Summary: The new class of CCFOs shows improved protective properties at intermediate temperatures, but also higher chemical reactivity with sand at elevated temperatures. The high-entropy oxide exhibits no grain boundary penetration at all temperatures, but significant reaction and precipitation occur. Additionally, these materials have higher thermal expansion coefficients compared to conventional materials.

JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2021)

Article Materials Science, Ceramics

A new class of high-entropy fluorite oxides with tunable expansion coefficients, low thermal conductivity and exceptional sintering resistance

Liang Xu et al.

Summary: The high-entropy fluorite-type oxides (HEFOs) synthesized in this study possess unique thermal expansion coefficients and low thermal conductivities, making them suitable candidates for high-temperature thermal barrier coatings and insulating materials.

JOURNAL OF THE EUROPEAN CERAMIC SOCIETY (2021)

Article Materials Science, Multidisciplinary

Glass-like thermal conductivity in mass-disordered high-entropy (Y, Yb)2(Ti, Zr, Hf)2O7 for thermal barrier material

Dowon Song et al.

Summary: The novel thermal insulating material YYHEO has low thermal conductivity, highly disordered crystal structure, and thermal expansion coefficient and mechanical properties comparable to traditional materials.

MATERIALS & DESIGN (2021)

Proceedings Paper Materials Science, Multidisciplinary

Thermal barrier coating for diesel engine application - A review

Vishwanath S. Godiganur et al.

Summary: Thermal barrier coatings (TBCs) show potential for improving efficiency in diesel engines, with different materials used to adapt to low-temperature applications compared to gas turbines. This paper provides a simplified review of the durability and reliability of TBCs in diesel engines, focusing on aspects such as residual stress, thermal cycling performance, thermal conductivity, and thermal reflectance.

MATERIALS TODAY-PROCEEDINGS (2021)

Article Biochemical Research Methods

SciPy 1.0: fundamental algorithms for scientific computing in Python

Pauli Virtanen et al.

NATURE METHODS (2020)

Article Physics, Multidisciplinary

Negative thermal expansion of Ca2RuO4with oxygen vacancies*

Sen Xu et al.

CHINESE PHYSICS B (2020)

Article Chemistry, Physical

Unavoidable disorder and entropy in multi-component systems

Cormac Toher et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Review Geochemistry & Geophysics

Thermodynamics with the Gruneisen parameter: Fundamentals and applications to high pressure physics and geophysics

Frank D. Stacey et al.

PHYSICS OF THE EARTH AND PLANETARY INTERIORS (2019)

Article Chemistry, Physical

Advanced structural ceramics in aerospace propulsion

Nitin P. Padture

NATURE MATERIALS (2016)

Article Chemistry, Physical

Room temperature lithium superionic conductivity in high entropy oxides

D. Berardan et al.

JOURNAL OF MATERIALS CHEMISTRY A (2016)

Article Physics, Multidisciplinary

Mechanism of negative thermal expansion in LaC2 from, first-principles prediction

Yaming Liu et al.

PHYSICS LETTERS A (2015)

Article Nanoscience & Nanotechnology

Microstructural development in equiatomic multicomponent alloys

B Cantor et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2004)

Article Materials Science, Multidisciplinary

Nanostructured high-entropy alloys with multiple principal elements: Novel alloy design concepts and outcomes

JW Yeh et al.

ADVANCED ENGINEERING MATERIALS (2004)