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301 results on '"Dong, Huafeng"'

1. Predicting Miscibility in Binary Compounds: A Machine Learning and Genetic Algorithm Study

Author

Feng, Chiwen, Liang, Yanwei, Sun, Jiaying, Wang, Renhai, Sun, Huaijun, and Dong, Huafeng

Subjects
Condensed Matter - Materials Science, Physics - Computational Physics
Abstract

The combination of data science and materials informatics has significantly propelled the advancement of multi-component compound synthesis research. This study employs atomic-level data to predict miscibility in binary compounds using machine learning, demonstrating the feasibility of such predictions. We have integrated experimental data from the Materials Project (MP) database and the Inorganic Crystal Structure Database (ICSD), covering 2,346 binary systems. We applied a random forest classification model to train the constructed dataset and analyze the key factors affecting the miscibility of binary systems and their significance while predicting binary systems with high synthetic potential. By employing advanced genetic algorithms on the Co-Eu system, we discovered three novel thermodynamically stable phases, CoEu8, Co3Eu2, and CoEu. This research offers valuable theoretical insights to guide experimental synthesis endeavors in binary and complex material systems.

Published
2024

3. High-throughput screening of strong electron-phonon couplings in ternary metal diborides

Author

Wang, Renhai, Sun, Yang, Zhang, Feng, Zheng, Feng, Fang, Yimei, Wu, Shunqing, Dong, Huafeng, Wang, Cai-Zhuang, Antropov, Vladimir, and Ho, Kai-Ming

Subjects
Condensed Matter - Superconductivity, Condensed Matter - Materials Science
Abstract

We perform a high-throughput screening on phonon-mediated superconductivity in ternary metal diboride structure with alkali, alkaline earth, and transition metals. We find 17 ground states and 78 low-energy metastable phases. From fast calculations of zone-center electron-phonon coupling, 43 compounds are revealed to show electron-phonon coupling strength higher than that of MgB2. An anti-correlation between energetic stability and electron-phonon coupling strength is identified. We suggest two phases, i.e., Li3ZrB8 and Ca3YB8, to be synthesized, which show reasonable energetic stability and superconducting critical temperature., Comment: 8 pages, 5 figures

Published
2022

5. Stability of xenon-sodium compounds at moderately low pressures

Author

Wang, Shaoxiong, Dong, Huafeng, Peng, Junhao, Wen, Minru, Zhang, Xin, Wu, Fugen, Goncharov, Alexander F., and Oganov, Artem R.

Subjects
Condensed Matter - Materials Science
Abstract

A growing body of theoretical and experimental evidence suggests that inert gases (He, Ne, Ar, Kr, Xe, Rn) become less and less inert under increasing pressure. Here we use the ab initio evolutionary algorithm to predict stable compounds of Xe and Na at pressures below 100 GPa, and find three stable compounds, NaXe, NaXe$_3$ and NaXe$_4$. The NaXe belongs to a well-known cubic CsCl structure type. The NaXe$_4$'s structure is common in amphiboles, whereas the NaXe$_3$ has a unique structure, analogous to the "post-perovskite" orthorhombic CaIrO$_3$-type structure with Ir atoms removed. This is the first time that a cation-vacant version of the CaIrO$_3$ is found in any compound. NaXe, NaXe$_3$ and NaXe$_4$ are found to be metallic.

Published
2022

46. Predicting miscibility in binary compounds: a machine learning and genetic algorithm study.

Author

Feng, Chiwen, Liang, Yanwei, Sun, Jiaying, Wang, Renhai, Sun, Huaijun, and Dong, Huafeng

Abstract

The combination of data science and materials informatics has significantly propelled the advancement of multi-component compound synthesis research. This study employs atomic-level data to predict miscibility in binary compounds using machine learning, demonstrating the feasibility of such predictions. We have integrated experimental data from the Materials Project (MP) database and the Inorganic Crystal Structure Database (ICSD), covering 2346 binary systems. We applied a random forest classification model to train the constructed dataset and analyze the key factors affecting the miscibility of binary systems and their significance while predicting binary systems with high synthetic potential. By employing advanced genetic algorithms on the Co–Eu system, we discovered three novel thermodynamically stable phases, CoEu8, Co3Eu2, and CoEu. This research offers valuable theoretical insights to guide experimental synthesis endeavors in binary and complex material systems. [ABSTRACT FROM AUTHOR]

Published
2025
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