Personal profile：

Huadong Fu, doctor of engineering, associate professor, doctoral supervisor. He received his bachelor’s degree and PhD degree in Materials Science and Engineering from University of Science and Technology Beijing. He worked in Tsinghua University as a Post-Doctoral Researcher from June 2012 to May 2014. He worked in Leicester University (UK) and Nagoya University (JP) as visiting professor from July 2016 to June 2017 and December 2020 to June 2021.

Research direction：

1. Intelligent design and processing technology of metals

2. Machine learning and Materials information science

3. Solidification and deformation of superalloys and copper-based alloys

Scientific research achievement :

He has presided over more than 20 scientific research projects including the National Natural Science Foundation of China, the National Key Research and Development Program of China and Beijing Nova Program. He has published more than 60 SCI search papers and obtained more than 20 national invention patents.

[1] Feng S, Fu H D*, Zhou H Y, et al. A general and transferable deep learning framework for predicting phase formation in materials [J]. npj Computational Materials, 2021, 7(1): 10.

[2] Zhang H T†, Fu H D†, He X Q, et al. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening[J]. Acta Materialia, 2020, 200: 803-810.

[3] Zhou X Z, Fu H D*, Xue F, et al. Abnormal precipitation of the μ phase during solution treatment of γ′-strengthened Co-Ni-Al-W-based superalloys[J]. Scripta Materialia, 2020, 181: 30-34.

[4] Fu H D, Zhang Y H, Xue F, et al. Microstructure and properties evolution of Co-Al-W-Ni-Cr superalloys by molybdenum and niobium substitutions for tungsten[J]. Metallurgical and Materials Transactions A, 2020, 51(1): 299-308.

[5] Wang C S†, Fu H D†, Jiang L, et al. A property-oriented design strategy for high performance copper alloys via machine learning[J]. npj Computational Materials, 2019, 5(1): 87.