CAO Bin 曹斌
is engaged in AI4CM computational materials research, specializing in crystal structure analysis and generation. He is keen to promote the unrestricted dissemination of knowledge and encourage transparent and accessible research.
My research interests include Physics-Informed Machine Learning Algorithms, X-ray diffraction, Simulation, Refinement, and Crystallography. Google Scholar Citations
I am currently studying at HKUST(GZ) under the supervision of Professor Zhang Tong-yi.
📝 Publications
CGWGAN: crystal generative framework based on Wyckoff generative adversarial network
Tianhao Su, Bin Cao(co-first author), Shunbo Hu, Musen Li, Tong-yi Zhang
- In this work, we present a crystal generative framework based on Wyckoff generative adversarial network (CGWGAN) to efficiently discover novel crystals.
- CGWGAN: crystal generative framework based on Wyckoff generative adversarial networkTianhao Su, Bin Cao(co-first author), Shunbo Hu, Musen Li, Tong-yi Zhang JMI
SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry Classification
Bin Cao, Yang Liu, Zinan Zheng, Ruifeng Tan, Jia Li, Tong-yi Zhang
- In this work, a large open-source dataset of powder XRD patterns designed for symmetry identification. 21 existing ML models are assessed, summarizing the XRD sequence data characteristics, and providing suggestions for the further development of ML models best suited for analyzing XRD patterns.
- SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystalline Symmetry ClassificationBin Cao, Yang Liu, Zinan Zheng, Ruifeng Tan, Jia Li, Tong-yi Zhang arXiv
Shouyang Zhang, Bin Cao (co-first), Tianhao Su, Yue Wu, Zhenjie Feng, Jie Xiong, Tong-Yi Zhang
- In this work, we developed a machine learning phase identifier that achieved excellent performance within a relatively small scope.
- Crystallographic Phase Identifier of a Convolutional Self-Attention Neural Network (CPICANN) on Powder Diffraction PatternsShouyang Zhang, Bin Cao (co-first), Tianhao Su, Yue Wu, Zhenjie Feng, Jie Xiong, Tong-Yi Zhang IUCrJ
B Cao, T Su, S Yu, T Li, T Zhang, Z Dong, TY Zhang
- To facilitate materials informatics development, all active learning algorithms were made open-source in our designed framework, Bgolearn…
- Active Learning Accelerates the Discovery of High Strength and High Ductility Lead-Free Solder Alloys B Cao, T Su, S Yu, T Li, T Zhang, Z Dong, TY Zhang Material & Design
MLMD: a programming-free AI platform to predict and design materials
Jiaxuan Ma, Bin Cao (co-first author), Shuya Dong, Yuan Tian, Menghuan Wang, Jie Xiong, Sheng Sun
- We developed MLMD, an AI platform for materials design. It is capable of effectively discovering novel materials with high-potential advanced properties end-to-end, utilizing model inference, surrogate optimization, and even working in situations of data scarcity based on active learning..
- MLMD: a programming-free AI platform to predict and design materials Ma, J., Cao, B(co-first)., Dong, S. et al. npj Comput Mater 10, 59 (2024)
Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility Qinghua Wei, Bin Cao (co-first author), Hao Yuan (co-first author), Youyang Chen, Kangdong You, Shuting Yu, Tixin Yang, Ziqiang Dong, Tong-Yi Zhang
- In general, small in size and big in noise, while the design space is huge, by a newly developed data preprocessing algorithm, named the Tree-Classifier for Gaussian Process Regression (TCGPR)…
- Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility. Qinghua Wei, Bin Cao (co-first author), Hao Yuan (co-first author), Youyang Chen, Kangdong You, Shuting Yu, Tixin Yang, Ziqiang Dong, Tong-Yi Zhang npj Comput Mater 201 (2023)
(Cover paper)Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water Bin Cao, Shuang Yang, Ankang Sun, Ziqiang Dong, Tong-Yi Zhang
- In this study, we propose a domain knowledge-guided interpretive machine learning strategy and demonstrate it by studying the oxidation behavior of ferritic-martensitic steels in supercritical water…
- Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water Cao B, Yang S, Sun A, Dong Z, Zhang TY. Journal of Materials Informatics(2022)
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