Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
H. Tahmasbi, A. Knüpfer, T. D. Kühne, H. Mirhosseini. "Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems." submitted (2026).
CV
Download: Short CV (technical) | Full CV (academic)
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
Work experience
- Postdoctoral Researcher, CASUS–HZDR, Görlitz, Germany, 2021–present
- High-throughput calculations with MongoDB/AiiDA for training ML interatomic potentials
- MLIP development for Aluminium and iron hydride
- Benchmarking universal ML interatomic potentials
- Postdoctoral Researcher, Leiden University, Netherlands, 2019–2021
- IR spectroscopic characterization of H₂ and CO₂ on cationic Cu clusters
- Systematic DFT studies aligned with experimental IR spectra
- Neural network potential simulations for large-scale atomic modeling
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)
- Visualization: VESTA, V-Sim, VMD
- Tools: Git, GitHub/GitLab, LaTeX, Jupyter Notebooks
Publications
Scalable machine learning model for energy decomposition analysis in aqueous systems
H. Tahmasbi, M. Beerbaum, B. Brzoza, A. Cangi, and T. D. Kühne. "Scalable machine learning model for energy decomposition analysis in aqueous systems." J. Chem. Phys. 163, 214115 (2025).
Machine Learning-Driven Structure Prediction for Iron Hydrides
H. Tahmasbi, K. Ramakrishna, M. Lokamani, and A. Cangi. "Machine Learning-Driven Structure Prediction for Iron Hydrides." Phys. Rev. Mater. 8, 033803 (2024).
Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter
S. Kumar, H. Tahmasbi, K. Ramakrishna, M. Lokamani, S. Nikolov, J. Tranchida, M. A. Wood, and A. Cangi. "Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter." Phys. Rev. Res. 5, 033162 (2023).
IR spectroscopic characterization of the co-adsorption of CO2 and H2 onto cationic Cu+n clusters
O. V. Lushchikova, M. Szalay, H. Tahmasbi, L. Juurlink, J. Meyer, T. Höltzl, and J. M. Bakker. "IR spectroscopic characterization of the co-adsorption of CO2 and H2 onto cationic Cu+n clusters." Phys. Chem. Chem. Phys. 23, 26661 (2021).
Large-Scale Structure Prediction of Near-Stoichiometric Magnesium Oxide Based on a Machine-Learned Interatomic Potential
H. Tahmasbi, S. Goedecker and S. A. Ghasemi. "Large-Scale Structure Prediction of Near-Stoichiometric Magnesium Oxide Based on a Machine-Learned Interatomic Potential: Crystalline Phases and Oxygen-Vacancy Ordering." Phys. Rev. Mater. 5, 083806 (2021).
An automated approach for developing neural network interatomic potentials with FLAME
H. Mirhosseini, H. Tahmasbi, S. R. Kuchana, S. A. Ghasemi, and T. D. Kühne. "An automated approach for developing neural network interatomic potentials with FLAME." Comput. Mater. Sci. 197, 110567 (2021).
IR Spectroscopic Characterization of H2 Adsorption on Cationic Cu+n (n=4-7) Clusters
O. V. Lushchikova, H. Tahmasbi, S. Reijmer, R. Platte, J. Meyer, and J. M. Bakker. "IR Spectroscopic Characterization of H2 Adsorption on Cationic Cu+n (n=4-7) Clusters." J. Phys. Chem. A 125, 2836 (2021).
FLAME: a library for atomistic modeling environments
M. Amsler, S. Rostami, H. Tahmasbi, E. Rahmatizad, S. Faraji, R. Rasoulkhani, and S. A. Ghasemi. "FLAME: a library for atomistic modeling environments." Comput. Phys. Commun. 256, 107415 (2020).
Two-Dimensional Hexagonal Sheet of TiO2
H. A. Eivari, S. A. Ghasemi, H. Tahmasbi, S. Rostami, S. Faraji, R. Rasoulkhani, S. Goedecker and M. Amsler. "Two-Dimensional Hexagonal Sheet of TiO2." Chem. Mater. 29, 8594 (2017).
Energy landscape of ZnO clusters and low density polymorphs
R. Rasoulkhani, H. Tahmasbi, S. A. Ghasemi, S. Faraji, S. Rostami and M. Amsler. "Energy landscape of ZnO clusters and low density polymorphs." Phys. Rev. B 96, 064108 (2017).
Solutions to Problems for ‘Statistical Mechanics, R. K. Pathria and Paul D. Beale (3rd Edition)’
Hossein Tahmasbi, Mohammad Behtaj. Solutions to Problems for "Statistical Mechanics, R. K. Pathria and Paul D. Beale (3rd Edition)". Hampa Press (2016).
Talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA, USA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley, CA, USA
Talk 1 on Relevant Topic in Your Field
Talk at UC San Francisco, Department of Testing, San Francisco, CA, USA