Education Background
Jul. 2010, Peking University, Ph.D. in Chemistry
Jul. 2006, Beijing Institute of Technology, Master of Engineering ( M.Eng.)
Jul. 2004, Beijing Institute of Technology, Bachelor of Engineering ( B.Eng.）
Work Experience
Mar.2021-Present, Tenured Associate Professor, School of Materials Science and Engineering, Peking University; School of Advanced Materials, Shenzhen Graduate School
Apr.2015-Mar.2021, Tenure-Track Investigator, College of Engineering, Peking University
Nov.2011-Mar.2015, Postdoctoral Researcher in Organic Chemistry, University of Texas at Austin, USA (*Advisor: Prof. Guangbin Dong)
Sep.2010-Jul.2011, Research Assistant in Chemical Biology, The Scripps Research Institute, USA (*Advisor: Prof. Qinghai Zhang)
Research Interests
The Mo Lab explores cutting-edge research at the intersection of emerging technologies and synthetic chemistry. Our group addresses efficiency bottlenecks in chemical synthesis, particularly chromatographic challenges, by integrating artificial intelligence. We have developed a series of automated platforms for chromatographic data collection and constructed diverse chromatography-specific datasets. Leveraging machine learning algorithms, we have trained generalizable, high-precision models capable of identifying optimal chromatographic conditions within seconds. This approach significantly reduces trial-and-error costs and accelerates research workflows.
Selected Publications
1. Wu W, Xu H, Xu Y, Luo P, Zeng Q, Chen Y, Xu Y, Zhang D and Mo F. Intelligent Column Chromatography Prediction Model Based on Automation and Machine Learning[J]. Chem, 2025, 11, in press.
2. Li H, Long D, Yuan L, Wang Y, Tian Y, Wang X and Mo F. Decoupled peak property learning for efficient and interpretable ECD spectra prediction[J]. Nature Computational Science, 2025, 5, 234-244.
3. Xu H, Wu W, Chen Y, Zhang D and Mo F. Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning[J]. Nature Communications, 2025, 16, 832.
4. Xu H, Lin J, Zhang D, Mo F. Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network[J]. Nature Communications, 2023, 14, 3095.
5. Xu H, Lin J, Liu Q, Chen Y, Zhang J, Yang Y, Young M. C, Xu, Y, Zhang D, Mo F. High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques[J]. Chem, 2022, 8, 3202-3214.
Message to Prospective Students
Stay hungry, stay foolish.
