Published in Hampa Press, 2016
Solutions manual for Pathria and Beale Statistical Mechanics.
Recommended citation: Hossein Tahmasbi, Mohammad Behtaj. Solutions to Problems for "Statistical Mechanics, R. K. Pathria and Paul D. Beale (3rd Edition)". Hampa Press (2016).
Published in Phys. Rev. B 96, 064108, 2017
Energy landscape exploration of ZnO clusters and polymorphs.
Recommended citation: 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).
Published in Chem. Mater. 29, 8594, 2017
Prediction of a 2D hexagonal TiO2 sheet.
Recommended citation: 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).
Published in Comput. Phys. Commun. 256, 107415, 2020
FLAME library for atomistic modeling environments.
Recommended citation: 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).
Published in J. Phys. Chem. A 125, 2836, 2021
IR spectroscopy of H2 adsorption on cationic copper clusters.
Recommended citation: 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).
Published in Comput. Mater. Sci. 197, 110567, 2021
Automated workflow for neural network interatomic potentials with FLAME.
Recommended citation: 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).
Published in Phys. Rev. Mater. 5, 083806, 2021
Large-scale structure prediction of MgO using ML interatomic potentials.
Recommended citation: 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).
Published in Phys. Chem. Chem. Phys. 23, 26661, 2021
IR spectroscopic study of CO2 and H2 co-adsorption on Cu clusters.
Recommended citation: 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).
Published in Phys. Rev. Res. 5, 033162, 2023
Transferable interatomic potentials for aluminum across extreme conditions.
Recommended citation: 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).
Published in Phys. Rev. Mater. 8, 033803, 2024
ML-driven structure prediction for iron hydrides.
Recommended citation: H. Tahmasbi, K. Ramakrishna, M. Lokamani, and A. Cangi. "Machine Learning-Driven Structure Prediction for Iron Hydrides." Phys. Rev. Mater. 8, 033803 (2024).
Published in J. Chem. Phys. 163, 214115, 2025
Machine learning model for energy decomposition in aqueous systems.
Recommended citation: 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).
Published in submitted, 2026
Benchmarking universal MLIPs on elemental systems.
Recommended citation: H. Tahmasbi, A. Knüpfer, T. D. Kühne, H. Mirhosseini. "Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems." submitted (2026).
A library for atomistic modeling environments. FLAME is a computational library for developing neural network interatomic potentials, structure prediction, and atomistic simulations.
Materials Learning Algorithms. A data-driven framework for predicting materials properties using machine learning.
An automated workflow for developing neural network interatomic potentials with FLAME. Streamlines the process of training and deploying MLIPs.
Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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