| Two-Dimensional Hexagonal Sheet of TiO2 HA Eivari, SA Ghasemi, H Tahmasbi, S Rostami, S Faraji, R Rasoulkhani, ... Chemistry of Materials 29 (20), 8594-8603, 2017 | 75 | 2017 |
| Energy landscape of ZnO clusters and low-density polymorphs R Rasoulkhani, H Tahmasbi, SA Ghasemi, S Faraji, S Rostami, M Amsler Physical Review B 96 (6), 064108, 2017 | 32 | 2017 |
| FLAME: a library of atomistic modeling environments M Amsler, S Rostami, H Tahmasbi, ER Khajehpasha, S Faraji, ... Computer Physics Communications 256, 107415, 2020 | 28 | 2020 |
| IR Spectroscopic Characterization of H2 Adsorption on Cationic Cun+ (n = 4–7) Clusters OV Lushchikova, H Tahmasbi, S Reijmer, R Platte, J Meyer, JM Bakker The Journal of Physical Chemistry A 125 (14), 2836-2848, 2021 | 22 | 2021 |
| IR spectroscopic characterization of the co-adsorption of CO 2 and H 2 onto cationic Cu n+ clusters OV Lushchikova, M Szalay, H Tahmasbi, LBF Juurlink, J Meyer, T Höltzl, ... Physical Chemistry Chemical Physics 23 (47), 26661-26673, 2021 | 9 | 2021 |
| An automated approach for developing neural network interatomic potentials with FLAME H Mirhosseini, H Tahmasbi, SR Kuchana, SA Ghasemi, TD Kühne Computational Materials Science 197, 110567, 2021 | 7 | 2021 |
| Large-scale structure prediction of near-stoichiometric magnesium oxide based on a machine-learned interatomic potential: Crystalline phases and oxygen-vacancy ordering H Tahmasbi, S Goedecker, SA Ghasemi Physical Review Materials 5 (8), 083806, 2021 | 7 | 2021 |
| Interatomic potentials based on artificial neural network: Structural and thermal properties of matters H Tahmasbi Institute for Advanced Studies in Basic Sciences (IASBS), PhD-Dissertation, 2019 | 2 | 2019 |
| Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter S Kumar, H Tahmasbi, K Ramakrishna, M Lokamani, S Nikolov, ... Physical Review Research 5 (3), 033162, 2023 | 1 | 2023 |
| Machine learning-driven structure prediction for iron hydrides H Tahmasbi, K Ramakrishna, M Lokamani, A Cangi Physical Review Materials 8 (3), 033803, 2024 | | 2024 |
| Structure prediction of iron hydrides across pressure range with transferable machine-learned interatomic potential H Tahmasbi, K Ramakrishna, M Lokamani, A Cangi Bulletin of the American Physical Society, 2024 | | 2024 |
| Machine learning-based quantum accurate interatomic potentials for warm dense matter S Kumar, H Tahmasbi, M Lokamani, K Ramakrishna, A Cangi APS March Meeting Abstracts 2023, N20. 003, 2023 | | 2023 |
| Structure prediction of ionic materials using the Minima Hopping method and the CENT machine learning potential SA Goedecker, H Tahmasbi, E Khajehpasha, S Rostami, H Asnaashari, ... APS March Meeting Abstracts 2021, M41. 008, 2021 | | 2021 |
S. Alireza GhasemiAssistant Professor of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.Bestätigte E-Mail-Adresse bei iasbs.ac.ir
