Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
Published:
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Solutions to Problems for ‘Statistical Mechanics, R. K. Pathria and Paul D. Beale (3rd Edition)’
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).
Energy landscape of ZnO clusters and low density polymorphs
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).
Two-Dimensional Hexagonal Sheet of TiO2
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).
FLAME: a library for atomistic modeling environments
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).
IR Spectroscopic Characterization of H2 Adsorption on Cationic Cu+n (n=4-7) Clusters
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).
An automated approach for developing neural network interatomic potentials with FLAME
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).
Large-Scale Structure Prediction of Near-Stoichiometric Magnesium Oxide Based on a Machine-Learned Interatomic Potential
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).
IR spectroscopic characterization of the co-adsorption of CO2 and H2 onto cationic Cu+n clusters
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).
Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter
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).
Machine Learning-Driven Structure Prediction for Iron Hydrides
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).
Scalable machine learning model for energy decomposition analysis in aqueous systems
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).
Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
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).
research
FLAME
A library for atomistic modeling environments. FLAME is a computational library for developing neural network interatomic potentials, structure prediction, and atomistic simulations.
MALA
Materials Learning Algorithms. A data-driven framework for predicting materials properties using machine learning.
PyFLAME
An automated workflow for developing neural network interatomic potentials with FLAME. Streamlines the process of training and deploying MLIPs.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.