构建深度学习和可解释机器学习模型，用于电子圆二色谱预测、谱峰特征学习和化学结构分析，提升分子谱学计算的效率与可解释性；研究分子自组装、催组装、超分子聚合以及手性传递，发展具有特定拓扑结构和功能的分子组装体。代表性论文：Decoupled peak property learning for efficient and interpretable electronic circular dichroism spectrum prediction, Nature Computational Science (2025)；Computed ECD spectral data for over 10,000 chiral organic small molecules, Scientific Data (2025)；What can molecular assembly learn from catalysed assembly in living organisms?, Chemical Society Reviews (2024)；Molecular Face-Rotating Polyhedra: Chiral Cages Inspired by Mathematics, Accounts of Chemical Research (2024)；Assembled molecular face-rotating polyhedra to transfer chirality from two to three dimensions, Nature Communications (2016)。