Prediction of sintered density of binary W(Mo) alloys using machine learning

Liu, He-Xiong; Yang, Yunfei; Cai, Yongfeng; Wang, Chang-hao; Lai, Chen; Hao, Yaowu; Wang, Jinshu

doi:10.1007/s12598-022-02238-0

Cited by 12 publications

(2 citation statements)

References 62 publications

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“…CuIr nanoalloys exhibit outstanding OER performance in strongly acidic conditions . Unfortunately, the increasing chemical space and structural complexity have prevented the screening of potential alloy electrocatalysts and the understanding of the intrinsic determinants of alloy catalytic activity. , At present, it is inefficient to design and develop excellent alloy electrocatalysts using traditional research methods, such as trial and error and the density functional theory (DFT) method. Therefore, it is necessary to establish a model to quantify the relationship between the intrinsic properties of alloys and their catalytic activity …”

Section: Introductionmentioning

confidence: 99%

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

Wang,

Wang

et al. 2024

ACS Appl. Mater. Interfaces

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The distinctive characteristics of alloy catalysts, encompassing composition, structure, and modifiable adsorption sites, present significant potential for the development of highly efficient electrocatalysts for oxygen evolution/reduction reactions [oxygen evolution reactions (OERs)/oxygen reduction reactions (ORRs)]. Machine learning (ML) methods can quickly establish the relationship between material features and catalytic activity, thus accelerating the development of alloy electrocatalysts. However, the current abundance of features presents a crucial challenge in selecting the most pertinent ones. In this study, we explored seven intrinsic features directly derived from the material's structure, with a specific focus on the chemical environment of active sites and their nearest neighbors. An accurate and efficient ML model to predict potential bifunctional oxygen electrocatalysts based on the intrinsic features of AB-type alloy active sites and intermediate free energies in the OERs/ORRs was established. These features possess clear physical and chemical meanings, closely linked to the electronic and geometric structures of active sites and neighboring atoms, thereby providing indispensable insights for the discovery of high-performance electrocatalysts. The ML model achieved R 2 scores of 0.827, 0.913, and 0.711 for the predicted values of the three intermediate (OH, O, OOH) free energies, with corresponding mean absolute errors of 0.175, 0.242, and 0.200 eV, respectively. These results indicate that the ML model exhibits high accuracy in predicting the intermediate free energies. Furthermore, the ML model exhibited a prediction efficiency 150,000 times faster than traditional density functional theory calculations. This work will offer valuable insights and a framework for facilitating the rapid design of potential catalysts by ML methods.

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Section: Introductionmentioning

confidence: 99%

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

Wang,

Wang

et al. 2024

ACS Appl. Mater. Interfaces

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“…Refractory metal targets are commonly produced using powder metallurgy techniques. This manufacturing process can be categorized into two methods: "top-down" and "bottomup" [9][10][11]13]. The "top-down" approach to fabricating refractory metal targets relies on the principle of severe plastic deformation.…”

Section: Introductionmentioning

confidence: 99%

Hot-Pressing Deformation Yields Fine-Grained, Highly Dense and (002) Textured Ru Targets

Liu,

Xu,

Zhou

et al. 2023

Materials

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Ruthenium (Ru) is a refractory metal that has applications in the semiconductor industry as a sputtering target material. However, conventional powder metallurgy methods cannot produce dense and fine-grained Ru targets with preferred orientation. Here, we present a novel method of hot-pressing deformation to fabricate Ru targets with high relative density (98.8%), small grain size (~4.4 μm) and strong (002) texture. We demonstrate that applying pressures of 30–40 MPa at 1400 °C transforms cylindrical Ru samples into disk-shaped targets with nearly full densification in the central region. We also show that the hardness and the (002)/(101) peak intensity ratio of the targets increase with the pressure, indicating enhanced mechanical and crystallographic properties. Our study reveals the mechanisms of densification and texture formation of Ru targets by hot-pressing deformation.

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Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder

Yin,

Li,

Yan

et al. 2024

Rare Met.

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Prediction of sintered density of binary W(Mo) alloys using machine learning

Cited by 12 publications

References 62 publications

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

Hot-Pressing Deformation Yields Fine-Grained, Highly Dense and (002) Textured Ru Targets

Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder

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