I am currently a Postdoctoral Researcher, working at the intersection of Machine Learning and Chemistry, affiliated with Stanford University & SLAC, based in Menlo Park, CA, and earned my PhD from Technical University of Denmark (DTU) in 2023.
- 📚 Interested in code developing on machine learning model for materials.
- 💡 Enjoy applying ML/AI techniques to scientific and engineering problems.
- 🔬 Expertise in ML/AI, global optimization, heterogeneous catalysis, reaction kinetics, and data‐driven modelling.
- IANN — an equivariant InterAtomic Neural Network potential framework package (Documentation)
- AdsNet — a message passing graph neural network for adsorption (Public soon)
- PCAT — a Practical Catalysis Toolkit package
- AlphaScheme — a alpha model for binding energy (private)
- PlotPackage — a python plot package in the field of catalysis (Deprecated)
- Graph neural network-accelerated multitasking genetic algorithm for optimizing alloy surfaces with adsorbates
- High-throughput Compositional Screening of Alloy surfaces
- Active learning cluster expansion and Monte Carlo simulated annealing for screening
- High throughput screening across different elements (Jupyter-notebook)
- Machine Learning and Data Mining(Project1, Project2) from DTU
- Deep Learning from DTU
- Machine Learning Operations from DTU
- Pytorch Deep Learning from Deep Learning Wizard
- Computational Modelling of Materials for Energy Applications from DTU
- Materials Design with Machine Learning and Artificial Intelligence from DTU
- Advanced Computational Tools from DTU
- Concepts in Heterogeneous Catalysis (Project) from DTU by Jens Nørskov and Thomas Bligaard
- Computational Physics from UNLV
- Autonomous materials discovery from DTU
- CAMD Summer School from DTU
- BIKE Workshop from DTU
- PeriodicTable — an interactive periodic table application
- TravelTracker — track and visualize your travel history
- MapCombiner — combine and overlay multiple maps
- MapParser — parse and extract map data
| Skills | Tools / Libraries |
|---|---|
| Programming | Python, C++, (html, css, javescript, typescript, PHP, Java) |
| ML / Data | PyTorch, scikit-learn, numpy, pandas, MySQL |
| Agentic AI | RAG, LangChain, LangGraph, LLM deployment, Skills, MCP |
| Scientific / Modeling | DFT (VASP, GPAW), molecular dynamics (LAMMPS), Monte Carlo, microkinetics model, Kinetic Monte Carlo |
| ML potential | FastPot, PaiNN, NequIP, MACE, EquiformerV2, AMP, N2P2 |
| Global optimization | Multi-tasking genetic algorithm, simulated annealing |
| Useful tools | ASE (Atomic Simulation Environment), Pymatgen |
| DevOps & Tools | Git, GitHub, Jupyter, Docker |
| Visualization | Matplotlib, seaborn, plotly |