Prof. ZHAO Haitao

Prof. ZHAO Haitao

AI & Robotics for Material Intelligence

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Machine learning-aided crystal facet rational design with ionic liquid controllable synthesis

Published in Small, 2021

This work uses machine learning to guide the rational design of crystal facets during ionic liquid-assisted synthesis.

Recommended citation: Lai, F. M., Sun, Z. H., Saji, S. E., He, Y. C., Yu, X. F., Zhao, H. T.*, Guo, H. B.*, & Yin, Z. Y.* (2021). "Machine learning-aided crystal facet rational design with ionic liquid controllable synthesis." Small, 17(12), 2100024.
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Data-driven materials innovation and applications

Published in Advanced Materials, 2022

This review highlights the role of data-driven approaches in accelerating materials discovery and applications.

Recommended citation: Wang, Z., Sun, Z. H., Yin, H., Liu, X. H., Wang, J. L., Zhao, H. T.*, Pang, C. H.*, Wu, T., Li, S. Z.*, Yin, Z. Y.*, & Yu, X. F. (2022). "Data-driven materials innovation and applications." Advanced Materials, 34(36), 2104113.
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Machine learning-enhanced flexible mechanical sensing

Published in Nano-Micro Letters, 2023

This paper presents a machine learning-enhanced strategy for improving the performance of flexible mechanical sensors.

Recommended citation: Wang, Y. J., Adam, M. L., Zhao, Y. L., Zheng, W. H., Gao, L. B.*, Yin, Z. Y.*, & Zhao, H. T.* (2023). "Machine learning-enhanced flexible mechanical sensing." Nano-Micro Letters, 15, 55.
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Data-driven structural descriptor for predicting platinum-based alloys as oxygen reduction electrocatalysts

Published in InfoMat, 2023

This work introduces a structural descriptor derived from data to predict Pt-based alloy performance in ORR catalysis.

Recommended citation: Zhang, X., Wang, Z., Adam, M. L., Wang, J. H., Hsieh, C. Y., Duan, C. R., Pang, C. H., Chu, P. K., Yu, X. F.*, & Zhao, H. T.* (2023). "Data-driven structural descriptor for predicting platinum-based alloys as oxygen reduction electrocatalysts." InfoMat, 5(6), e12406.
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A robotic platform for the synthesis of colloidal nanocrystals

Published in Nature Synthesis, 2023

This work presents a robotic platform for synthesizing colloidal nanocrystals with high reproducibility and automation.

Recommended citation: Zhao, H. T.*, Chen, W., Huang, H., Sun, Z. H., Chen, Z. J., Wu, L. J., Zhang, B. C., Lai, F. M., Wang, Z., Adam, M. L., Pang, C. H., Chu, P. K., Lu, Y., Wu, T., Jiang, J.*, Yin, Z. Y.*, & Yu, X. F*. (2023). "A robotic platform for the synthesis of colloidal nanocrystals." Nature Synthesis, 2(6), 505–514.
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Robotic platform for accelerating the high-throughput study of silver nanocrystals in sensitive/selective Hg²⁺ detection

Published in Chemical Engineering Journal, 2023

A robotic platform enables high-throughput experimentation for silver nanocrystals in mercury ion detection.

Recommended citation: Xing, L. X., Chen, Z. J., Chen, W., Chu, P. K., Yu, X. F.*, & Zhao, H. T.* (2023). "Robotic platform for accelerating the high-throughput study of silver nanocrystals in sensitive/selective Hg²⁺ detection." Chemical Engineering Journal, 466, 143225.
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Machine learning and robot-assisted synthesis of diverse gold nanorods via seedless approach

Published in Artificial Intelligence Chemistry, 2023

This work combines machine learning and robotic synthesis to produce a wide range of gold nanorods without seeds.

Recommended citation: Moses, O. A., Adam, M. L., Chen, Z. J., Ezeh, C. I., Huang, H., Wang, Z., Wang, Z. Y., Wang, B. Y., Li, W. T., Wang, C. S., Yin, Z. Y.*, Lu, Y.*, Yu, X. F.*, & Zhao, H. T.* (2023). "Machine learning and robot-assisted synthesis of diverse gold nanorods via seedless approach." Artificial Intelligence Chemistry, 2, 100028.
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Navigating materials chemical space to discover new battery electrodes using machine learning

Published in Energy Storage Materials, 2024

This study leverages machine learning to explore the chemical space for discovering new battery electrode materials.

Recommended citation: Adam, M. L., Moses, O. A., Mailoa, J. P., Hsieh, C. Y.*, Yu, X. F.*, Li, H.*, & Zhao, H. T.* (2024). "Navigating materials chemical space to discover new battery electrodes using machine learning." Energy Storage Materials, 65, 103090.
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Data-driven controlled synthesis of oriented quasi-spherical CsPbBr₃ perovskite materials

Published in Angewandte Chemie International Edition, 2024

This paper demonstrates a data-driven approach to control the synthesis of CsPbBr₃ perovskite with specific orientation and morphology.

Recommended citation: Liu, S. H., Chen, Z. J., Liu, Y. M., Wu, L. J., Wang, B. Y., Wang, Z. Y., Wu, B. B., Zhang, X. Y., Zhang, J., Chen, M. Y., Huang, H.*, Ye, J. Z.*, Chu, P. K., Yu, X. F., Polavarapu, L., Robert, L. Z. H., Gao, F., & Zhao, H. T.* (2024). "Data-driven controlled synthesis of oriented quasi-spherical CsPbBr₃ perovskite materials." Angewandte Chemie International Edition, 63(14), e202319480.
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Data-driven fine element tuning of halide double perovskite for enhanced photoluminescence

Published in Advanced Optical Materials, 2024

A machine learning-guided strategy to tune halide double perovskites for improved photoluminescence properties.

Recommended citation: Wu, L. J., Chen, Z. J., Yuan, Z. C., Wu, B. B., Liu, S. H., Wang, Z. X., Mailoa, J. P., Duan, C. R., Huang, H.*, Hsieh, C. Y.*, Yu, X. F.*, & Zhao, H. T.* (2024). "Data-driven fine element tuning of halide double perovskite for enhanced photoluminescence." Advanced Optical Materials, 12(8), 2301245.
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Digital manufacturing of perovskite materials and solar cells

Published in Applied Energy, 2025

This paper discusses digital manufacturing strategies for scalable and efficient production of perovskite solar cells.

Recommended citation: Wang, Z. Y., Chen, Z. J., Wang, B. Y., Wu, C., Zhou, C., Peng, Y., Zhang, X. Y., Ni, Z. M., Chung, C. Y., Chan, C. C., Yang, J.*, & Zhao, H. T.* (2025). "Digital manufacturing of perovskite materials and solar cells." Applied Energy, 377, 124120.
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