| Stratified construction of neural network based interatomic models for multicomponent materials S Hajinazar, J Shao, AN Kolmogorov Phys. Rev. B 95, 014114, 2017 | 93 | 2017 |
| Electron–phonon physics from first principles using the EPW code H Lee, S Poncé, K Bushick, S Hajinazar, J Lafuente-Bartolome, ... npj Computational Materials 9 (1), 156, 2023 | 77 | 2023 |
| MAISE: Construction of neural network interatomic models and evolutionary structure optimization S Hajinazar, A Thorn, ED Sandoval, S Kharabadze, AN Kolmogorov Computer Physics Communications 259, 107679, 2021 | 49 | 2021 |
| Stability of two-dimensional BN-Si structures ED Sandoval, S Hajinazar, AN Kolmogorov Physical Review B 94 (9), 094105, 2016 | 38 | 2016 |
| Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models S Hajinazar, ED Sandoval, AJ Cullo, AN Kolmogorov Physical Chemistry Chemical Physics 21 (17), 8729-8742, 2019 | 33 | 2019 |
| Structural search for stable Mg–Ca alloys accelerated with a neural network interatomic model W Ibarra-Hernández, S Hajinazar, G Avendaño-Franco, ... Physical Chemistry Chemical Physics 20 (43), 27545-27557, 2018 | 27 | 2018 |
| Toward ab Initio Ground States of Gold Clusters via Neural Network Modeling A Thorn, J Rojas-Nunez, S Hajinazar, SE Baltazar, AN Kolmogorov The Journal of Physical Chemistry C 123 (50), 30088-30098, 2019 | 26 | 2019 |
| Synthesis of a predicted layered LiB via cold compression AN Kolmogorov, S Hajinazar, C Angyal, VL Kuznetsov, AP Jephcoat Physical Review B 92 (14), 144110, 2015 | 26 | 2015 |
| Full-bandwidth anisotropic Migdal-Eliashberg theory and its application to superhydrides R Lucrezi, PP Ferreira, S Hajinazar, H Mori, H Paudyal, ER Margine, ... Communications Physics 7 (1), 33, 2024 | 18 | 2024 |
| Inhomogeneous Kondo-lattice in geometrically frustrated Pr2Ir2O7 M Kavai, J Friedman, K Sherman, M Gong, I Giannakis, S Hajinazar, H Hu, ... Nature communications 12 (1), 1377, 2021 | 18 | 2021 |
| Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases B Wang, KP Hilleke, S Hajinazar, G Frapper, E Zurek Journal of Chemical Theory and Computation 19 (21), 7960–7971, 2023 | 4 | 2023 |
| Powder X-ray diffraction assisted evolutionary algorithm for crystal structure prediction S Racioppi, A Otero-de-la-Roza, S Hajinazar, E Zurek Digital Discovery 4 (1), 73-83, 2025 | 2 | 2025 |
| XtalOpt Version 13: Multi-Objective Evolutionary Search for Novel Functional Materials S Hajinazar, E Zurek Computer Physics Communications 304, 109306, 2024 | 2 | 2024 |
| Impact of data bias on machine learning for crystal compound synthesizability predictions A Davariashtiyani, B Wang, S Hajinazar, E Zurek, S Kadkhodaei Machine Learning: Science and Technology 5 (4), 040501, 2024 | | 2024 |
| Development of neural network interatomic potentials for accelerated prediction of stable compounds S Kharabadze, A Thorn, E Sandoval, S Hajinazar, A Kolmogorov APS March Meeting Abstracts 2022, F47. 007, 2022 | | 2022 |
| Anisotropic superconductivity calculations with the full-bandwidth Migdal-Eliashberg formalism using the EPW code S Hajinazar, H Paudyal, E Margine APS March Meeting Abstracts 2022, A57. 003, 2022 | | 2022 |
| MAISE package: Materials prediction accelerated with neural network potentials A Kolmogorov, S Hajinazar, E Sandoval APS March Meeting Abstracts 2019, X19. 002, 2019 | | 2019 |
| Co-evolutionary search for Cu-Pd-Ag nanoparticle ground states accelerated with neural network potentials A Cullo, S Hajinazar, E Sandoval, A Kolmogorov APS March Meeting Abstracts 2019, G70. 303, 2019 | | 2019 |
| Identification of stable Cu-Pd-Ag nanoparticles using neural network interatomic potentials S Hajinazar, E Sandoval, A Cullo, A Kolmogorov APS March Meeting Abstracts 2019, E22. 014, 2019 | | 2019 |
| Stratified Construction of Neural Network Potentials and Co-Evolutionary Global Structure Optimization for Acceleration of Ab Initio Materials Prediction S Hajinazar State University of New York at Binghamton, 2019 | | 2019 |