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Your search keyword '"Huang, Haiyou"' showing total 21 results
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21 results on '"Huang, Haiyou"'

2. Searching High Temperature Superconductors with the assistance of Graph Neural Networks

Author

Gu, Liang, Liu, Yang, Chen, Pin, Huang, Haiyou, Chen, Ning, Li, Yang, Lu, Yutong, and Su, Yanjing

Subjects
Condensed Matter - Superconductivity
Abstract

Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine learning models predicting Tc, which greatly improves the efficiency of prediction. Unfortunately, prevailing models have not shown adequate physical rationality and generalization ability to find new high temperature superconductors, yet. In this work, in order to give a trustable prediction on the unexplored materials, we built a bond-sensitive graph neural network (BSGNN), which is optimized to process the information of chemical bond and electron interaction in the crystal lattice, to predict the Tc maximum of each type of superconducting materials. On the basis of the domain knowledge considered in the data preparation and algorithm design, our model revealed a relevance between the Tc-Tc maximum and chemical bonds. The results indicate that shorter bond length is favored by high Tc, which is in accordance with previous human experience. Moreover, it also shows that some specific chemical elements are favored by high Tc, which is beyond what human experts already knew. It gives a convenient guidance for searching high temperature superconductors in materials database, by ruling out the materials that could never have high Tc.

Published
2023

12. Phase, Microstructure and Corrosion Behaviour of Al 0.3 FeCoNiCr x High-Entropy Alloys via Cr Addition.

Author

Chen, Mengyao, Shen, Haicheng, Wen, Cheng, Wang, Nanchuan, Tian, Yuwan, Zhong, Weihua, and Huang, Haiyou

Subjects
ELECTRON microscope techniques, FACE centered cubic structure, SCANNING electron microscopes, CORROSION in alloys, CORROSION resistance
Abstract

The microstructure evolution of Al0.3FeCoNiCrx (x = 0, 0.3, 0.5, 1, 1.5) high-entropy alloys (HEAs) were studied using X-Ray diffraction technique and scanning electron microscope equipped with energy dispersive spectrometer. The short-term and long-term corrosion behaviours of these alloys in 3.5 wt.% NaCl solution were studied by electrochemical impedance spectroscopy, potentiodynamic polarisation measurement, immersion test and corrosion morphology analysis. The results show that all the designed HEAs present single-phase FCC structure, and the increase in Cr content changes the microstructural morphology from cellular to a typical dendritic–interdendritic state. Without the influence of phase transformation, the corrosion resistance of Al0.3FeCoNiCrx HEAs gradually increases with the increase in Cr content. Our designed alloys exhibit excellent corrosion resistance compared to the existing HEAs in the AlFeCoNiCr composition system. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Machine-Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys.

Author

Gao, Yingyu, Hu, Yunfeng, Zhao, Xinpeng, Liu, Yang, Huang, Haiyou, and Su, Yanjing

Subjects
MACHINE learning, ALLOYS, ENTROPY, REFRIGERANTS, REFRIGERATION & refrigerating machinery
Abstract

In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. However, conventional methods for alloy development are inefficient, often failing to meet the stringent requirements for practical applications. This study employed machine learning (ML) to accelerate the design of NiTi-based SMAs with an enhanced elastocaloric effect. Through active learning across four iterations, we identified nine novel NiTi-based SMAs exhibiting phase-transformation-induced entropy changes (ΔS) greater than 90 J/kg·K−1, surpassing most existing alloys. Our ML model demonstrates robust interpretability, revealing key relationships between material features and performance. This work not only establishes a more efficient pathway for alloy discovery but also aims to contribute significantly to the advancement of sustainable refrigeration technologies. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Machine-Learning-Assisted Compositional Design of Refractory High-Entropy Alloys with Optimal Strength and Ductility

Author

Wen, Cheng, Zhang, Yan, Wang, Changxin, Huang, Haiyou, Wu, Yuan, Lookman, Turab, and Su, Yanjing

Abstract

Designing refractory high-entropy alloys (RHEAs) for high-temperature (HT) applications is an outstanding challenge given the vast possible composition space, which contains billions of candidates, and the need to optimize across multiple objectives. Here, we present an approach that accelerates the discovery of RHEA compositions with superior strength and ductility by integrating machine learning (ML), genetic search, cluster analysis, and experimental design. We iteratively synthesize and characterize 24 predicted compositions after six feedback loops. Four compositions show outstanding combinations of HT yield strength and room-temperature (RT) ductility spanning the ranges of 714–1061 MPa and 17.2%–50.0% fracture strain, respectively. We identify an attractive alloy system, ZrNbMoHfTa, particularly the composition Zr0.13Nb0.27Mo0.26Hf0.13Ta0.21, which demonstrates a yield approaching 940 MPa at 1200 °C and favorable RT ductility with 17.2% fracture strain. The high yield strength at 1200 °C exceeds that reported for RHEAs, with 1200 °C exceeding the service temperature limit for Ni-based superalloys. Our ML-based approach makes it possible to rapidly optimize multiple properties for materials design, thus overcoming the common problems of limited data and a vast composition space in complex materials systems while satisfying multiple objectives.

Published
2024
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