Computational chemistry · Reactive MLIPs · AI for Science
Chemical Engineering PhD candidate at Dalian University of Technology · Expected 2027
I work at the intersection of quantum chemistry, reactive machine-learning potentials, and computer-aided molecular design. My focus is building reproducible workflows for transition-state search and organometallic catalyst design, connecting computational models with experimentally testable chemistry.
Email · Google Scholar · ORCID · GitHub
- Reactive MLIPs — model development, data generation, evaluation, and scientific workflow integration.
- Transition-state search — automated TS initial guesses, optimization, IRC validation, and basin-aware screening.
- Organometallic catalysis — mechanism-aware descriptors, ligand screening, and structure–activity analysis.
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Reactive machine learning potential for accelerating transition state search in organic synthesis
Nature Communications · 2026 · DOI -
Accelerating Transition State Search and Ligand Screening for Organometallic Catalysis with Reactive Machine Learning Potential
Journal of Chemical Theory and Computation · 2025 · DOI -
GC-NORM-based thermodynamic framework for evaluations of organic reactions involving carbon dioxide utilization
Chemical Engineering Science · 2023 · DOI
Python · PyTorch · ASE · PySCF · Gaussian · ORCA · RDKit · Linux · Slurm · Git/GitHub
I also write code and research notes on AI for Science, molecular modelling, scientific software, and reliable computational chemistry workflows.