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

Nov.2025,Tenured Associate Professor, School of AI for Science , Peking University

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 developeda series ofautomated platformsfor 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.

Honors & Awards

2022 Cell Press China Best Paper Award

2021 European Young Talent Award in Organic Chemistry

2020-nowStanford University World's Top 2% Scientists List

2020 KGKF Award for Mentoring Excellence, Peking University

Selected Publications

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.

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.

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.

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.

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.