CV
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Computational materials scientist with a Ph.D. in condensed-matter physics and 7 years of postdoctoral research experience at CASUS–HZDR and Leiden University. I develop — and, just as importantly, evaluate — machine learning interatomic potentials (MLIPs): benchmarking foundation models for materials, building high-throughput DFT/ML workflows, and validating predictions against both ab initio and experimental data.
Work experience
- Postdoctoral Researcher, CASUS–HZDR, Görlitz, Germany, 2021–present
- Benchmarked six universal MLIPs across 15 elemental systems, defining metrics for stability, transferability, and energetic ordering
- Trained MLIPs for aluminium and iron hydride using a MongoDB/AiiDA high-throughput workflow
- Developed a PyTorch neural network for water energy-decomposition analysis, scaling predictions beyond DFT reach
- Postdoctoral Researcher, Leiden University, Netherlands, 2019–2021
- Combined DFT with experiments to assign IR spectra of H₂ and CO₂ on cationic copper clusters
- Ran neural-network-potential simulations for large-scale atomic modeling
Education
- Ph.D. in Hard Condensed-Matter Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran, 2014–2019
- Thesis: Interatomic Potentials based on Artificial Neural Network: Structural and Thermal Properties of Matters
- Visiting Ph.D. Researcher at Leiden University, Netherlands (2017–2018)
- M.Sc. in Solid-State Physics, Iran University of Science and Technology (IUST), Tehran, Iran, 2005–2008
- B.Sc. in Applied Physics, Arak University, Arak, Iran, 2001–2005
Skills
- Programming: Python (NumPy, SciPy, Pandas, PyTorch), FORTRAN, Bash
- HPC & Workflows: Slurm, GPU computing, AiiDA, MongoDB
- DFT & MD: FHI-aims, VASP, LAMMPS, PHONOPY
- ML: Neural Network Potentials, Machine Learning Interatomic Potentials (MLIPs), model benchmarking & validation
- Visualization: VESTA, V-Sim, VMD
- Tools: Git, GitHub/GitLab, LaTeX, Jupyter Notebooks
- Open-source: aiida-gw, ALMO_EDA, FLAME, PyFLAME
Languages
- Persian (native), English (fluent), German (B2)
Publications
- Full list available on Google Scholar