姓名：杨骐

性别：男

职称:研究员

研究领域：人工智能、AI for Science、面向化学场景的大模型与算法研发

联系方式：yangqi@hlsct.cn

个人简介：

博士，研究员，硕士研究生导师。博士毕业于中国科学院化学研究所，聚焦“AI +化学”交叉研究方向；2019年7月至2024年7月于宝洁公司研发中心历任科学家、高级科学家，从事人工智能与大数据技术的研发及落地应用研究。当前研究工作以人工智能为核心，以化学、材料等领域为典型落地应用场景，重点围绕科学垂域大模型、智能体系统、Data-centric AI范式、科学数据治理、垂直场景专用算法与智能工具软件开发开展系统性研究，持续推动AI for Science领域的前沿技术创新与产业应用转化。迄今已在Nature Communications、Nature Synthesis等国际顶级学术期刊发表论文20余篇，h-index 14，授权及公开发明专利2项，计算机软件著作权1项，主持并参与多项国家级、省部级自然科学基金项目。

代表性论文：

[1]Z. Tan,Q. Yang*, L. Zhang, S. Luo*, “Synthetic applicability domain (SynAD): Navigating chemical space for reliable AI-driven reaction prediction”Angew. Chem. Int. Ed.2026, e23874.

[2]J. Li, M. Li,Q. Yang*, S. Luo*, “ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis”Nat. Commun.2026, DOI 10.1038/s41467-026-70180-1.

[3]H. Xu, T. Du, J. Lin, F. You, Y. Shao,Q. Yang*, X. Li*, “Physicochemically informed axial chirality descriptors enable accurate prediction of atropisomeric stability”Angew. Chem. Int. Ed.2025, e21349.

[4]Z. Tan,Q. Yang*, S. Luo*, “AI molecular catalysis: where are we now?”Org. Chem. Front.2025,12, 2759–2776.

[5]Y. Shao, H. Xu, F. You, Y. Li,Q. Yang*, X. Xue*, “Machine Learning‐Based Prediction of Bond Dissociation Energies for Metal‐Trifluoromethyl Compounds”Chin. J. Chem.2025,43, 1363–1372.

[6]Z. Qiu, W. Wang,Q. Yang, J. Zheng, X. Zhao, J. Dang*, “Machine Learning Interatomic Potential‐Enabled Discovery of Chlorofullerenes”Chemistry A European J2025,31, e202501632.

[7]S. Liu,Q. Yang*, L. Zhang, S. Luo*, “Highly Precise Prediction of Micro‐ and Supra‐pKaBased on 3D Descriptors Integrating Non‐Covalent Interactions”Angew. Chem. Int. Ed.2025,64, e202424069.

[8]L. Cheng, Z. Tan, Z. Jia, Q. Lin,Q. Yang*, S. Luo*, “Heuristic data-driven approach for synergistic cobalt(IV)–enamine catalysis”Nat. Synth.2025, 1–11.

[9]Y. Liu, Y. Li, Q. Yang, J. Yang,L. Zhang*, S. Luo*, “Prediction of Bond Dissociation Energy for Organic Molecules Based on aMachine‐LearningApproach”Chin. J. Chem.2024,42, 1967–1974.

[10]S. Liu,Q. Yang*, L. Zhang, S. Luo*, “Accurate Protein pKaPrediction with Physical Organic Chemistry Guided 3D Protein Representation”J. Chem. Inf. Model.2024,64, 4410–4418.

[11]X. Hong,Q. Yang*, K. Liao, J. Pei, M. Chen, F. Mo, H. Lu, W.-B. Zhang, H. Zhou, J. Chen, L. Su, S.-Q. Zhang, S. Liu, X. Huang, Y.-Z. Sun, Y. Wang, Z. Zhang, Z. Yu, S. Luo, X.-F. Fu, S.-L. You “AI for organic and polymer synthesis”Sci. China Chem.2024,67, 2461–2496.

[12]Y. Liu,Q. Yang, J. Cheng, L. Zhang, S. Luo, J. Cheng, “Prediction of Nucleophilicity and Electrophilicity Based on a Machine‐Learning Approach”ChemPhysChem2023,24, e202300162.

[13]Q. Yang, Y. Liu, J. Cheng, Y. Li, S. Liu, Y. Duan, L. Zhang, S. Luo, “An ensemble structure and physicochemical (SPOC) descriptor for machine‐learning prediction of chemical reaction and molecular properties”ChemPhysChem2022,23, e202200255.

[14]Q. Yang, Y. Li, J. Yang, Y. Liu, L. Zhang, S. Luo, J. Cheng, “Holistic prediction of the pKain diverse solvents based on a machine‐learning approach”Angew. Chem. Int. Ed.2020,59, 19282–19291.

[15]Y. Liu#,Q. Yang#, Y. Li, L. Zhang, S. Luo, “Application of Machine Learning in Organic Chemistry”Chin. J. Org. Chem.2020,40, 3812.

招生与培养信息：

欢迎对人工智能、深度学习、AI for Science领域有浓厚科研兴趣的同学报考课题组硕士研究生！课题组研究工作以人工智能为核心，科学领域为技术落地场景，无化学相关专业背景要求。依托学院与海河实验室完善的科研平台与充足的科研经费，兼具学术研究能力与工业落地实践经验，可为学生提供定制化的培养方案、全方位的科研能力训练与丰富的国内外学术、产业合作交流机会。