Professor Yuzhou Liu received his bachelor’s and master’s degrees from the Department of Chemistry, Tsinghua University, and his doctorate from the Department of Chemistry, New York University. He is currently a Professor and doctoral supervisor at the School of Chemistry, Beihang University. He has been selected for the National High‑level Talent Program, awarded the National Outstanding Self‑Funded Overseas Student honor, and recognized as a Science and Technology Leading Talent of Suzhou Industrial Park.

He has published original research papers in top‑tier journals including Science and Nature family journals, and holds more than 40 international patents. In the field of molecular design and synthesis, he has successfully developed organosilicon materials featuring high hardness and high stability, breaking international monopolies, and achieved major breakthroughs in platinum‑catalyzed reactions.

Professor Liu has long been committed to applying artificial intelligence and automated robots to the development of new materials, making significant advances in intelligent material design, high‑throughput computation and experimental automation. He leads the development of an industry‑leading automated high‑throughput design‑and‑synthesis platform, which has received multiple honors including the Beijing Artificial Intelligence Industry Empowerment Typical Case, Demonstration Case of the Beijing National Artificial Intelligence Innovation and Application Pilot Zone, and the AI Golden Wild Goose Award for Application Innovation. The platform has secured multiple rounds of venture capital investment.

He has developed and commercialized multiple market‑competitive new‑material products, and provides R&D services for numerous listed enterprises, Grade‑A tertiary hospitals and universities. He presides over a host of national‑level research projects such as the National Natural Science Foundation of China and the National Key R&D Program. As a pioneer in the cutting‑edge “AI‑plus” technology sector, he participated in compiling the Beijing Action Plan for Accelerating the Innovative Development of “AI‑plus New Materials” (2025‑2027). His innovative work deeply integrates machine learning and multi‑scale modeling with materials science, delivering technical support for the intelligent R&D of novel functional materials.

His self‑developed Yun Platform (Yun‑YunSuan, website: https://aiyun.syntelligence.cn/) is built upon deep learning combined with fundamental materials‑science theories including Density Functional Theory (DFT) and first‑principles calculations. Trained on massive high‑precision chemical datasets, the platform automatically and accurately performs multi‑step transition‑state searching, novel‑molecule generation, high‑throughput exploration, reaction and catalyst screening, synthetic‑route optimization, molecular‑property prediction and other functions.It supports a comprehensive suite of computational approaches, including first‑principles methods, Density Functional Theory (DFT), and Ab‑initio Molecular Dynamics (AIMD). It dynamically selects optimal algorithms and parameters according to reactant structures, system types and required calculation precision. The platform has overcome long‑standing technical challenges such as bond‑energy analysis and multi‑step transition‑state searching. It achieves an activation‑energy prediction standard deviation of 0.76 kcal/mol and a reaction‑path prediction accuracy up to 95 %, reaching chemical‑experiment‑grade performance. Operated entirely via web browser, it delivers intuitive, fast and convenient workflows with one‑click report generation.

Currently, the research group is recruiting postdoctoral researchers, doctoral students, and master's students. Applicants from various backgrounds are welcome, including chemistry, materials science, environmental science, and computational science.

Professor Liu Yuzhou has developed a supramolecular Archimedean cage (quasi-truncated octahedron, q-TO) assembled via 72 hydrogen bonds. Composed of 20 ions from three distinct species, this cage serves as a building block for body-centered cubic (bcc)-type zeolite-like frameworks. It can encapsulate a wide range of molecules, metal complexes, and nanoclusters with varying charges, shapes, and sizes. Notably, the framework assembly exhibits intrinsic thermodynamic stability and is not influenced by guest templates.Liu, Y., Hu, C., Comotti, A., & Ward, M. D. (2011). Supramolecular Archimedean Cages Assembled with 72 Hydrogen Bonds. Science, 333(6041), 436. https://doi-org-443.e1.buaa.edu.cn/10.1126/science.1204369.

A biomimetic caged platinum catalyst (COP1-T-Pt) developed by our research group is based on a truncated octahedron model. By encapsulating platinum atoms within a porous cage ligand, an enzyme-mimetic microenvironment is formed. The catalyst exhibits approximately 12-fold higher activity than the Karstedt catalyst in the catalytic hydrosilylation reaction and is recyclable. It simultaneously possesses size selectivity, Michaelis-Menten kinetic characteristics, and high site selectivity, enabling adaptation to a variety of substrates containing multiple functional groups. This work provides a new strategy for the design of highly selective catalysts.Pan, G., Hu, C., Hong, S., Li, H., Yu, D., Cui, C., Li, Q., Liang, N., Jiang, Y., Zheng, L., Jiang, L., & Liu, Y. (2021). Biomimetic caged platinum catalyst for hydrosilylation reaction with high site selectivity. Nature Communications, 12(64). https://doi-org-443.e1.buaa.edu.cn/10.1038/s41467-020-20233-w.

Our research group has developed a 2D porous carbon material (PBN) based on polyhexaphenylbenzene. Through thermal self-healing, PBN forms a conductive carbon support (PBN-300). The Ir single-atom catalyst supported on this material (PBN-300-Ir) exhibits superior activity and stability compared to commercial Pt/C and Ir/C catalysts in the hydrogen evolution reaction (HER). At a current density of 10 mA/cm², the overpotential is merely 17 mV; at 70 mV, the mass activity reaches 51.6 A mgIr⁻¹, and the turnover frequency (TOF) is 170.61 s⁻¹ at 100 mV. Density functional theory (DFT) calculations confirm that the coordination interaction between carbon and the Ir centers is the core mechanism underlying the high catalytic activity. Furthermore, this synthetic strategy can be extended to the preparation of various transition metal single-atom catalysts.Liu, C., Pan, G., Liang, N., Hong, S., Ma, J., & Liu, Y. (2022). Ir Single Atom Catalyst Loaded on Amorphous Carbon Materials with High HER Activity. Advanced Science, 9(2105392). https://doi-org-443.e1.buaa.edu.cn/10.1002/advs.202105392.

This study reports the one-step synthesis of novel cyclic polysiloxanes containing linked cyclotetrasiloxane subunits via the Piers–Rubinsztajn reaction. By optimizing the reactant addition mode and introducing self-aggregating fluorinated groups, the polymer dispersity index (PDI) is reduced (minimum PDI = 1.4). The thiolated products can effectively induce gold nanoparticles to form stable and soluble cyclic assemblies, providing a new pathway for the structural expansion and functional application of cyclic polymers.Yu, J., & Liu, Y. (2017). Cyclic Polysiloxanes with Linked Cyclotetrasiloxane Subunits. Angewandte Chemie International Edition, 56(28), 8706–8710. https://doi-org-443.e1.buaa.edu.cn/10.1002/anie.201703347.