| Predicting synthesizability of crystalline materials via deep learning A Davariashtiyani, Z Kadkhodaie, S Kadkhodaei Communications Materials 2 (1), 115, 2021 | 26 | 2021 |
| Phonon-assisted diffusion in bcc phase of titanium and zirconium from first principles S Kadkhodaei, A Davariashtiyani Physical Review Materials 4 (4), 043802, 2020 | 17 | 2020 |
| Understanding the role of anharmonic phonons in diffusion of bcc metals S Fattahpour, A Davariashtiyani, S Kadkhodaei Physical Review Materials 6 (2), 023803, 2022 | 4 | 2022 |
| Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation A Davariashtiyani, S Kadkhodaei Communications Materials 4 (1), 105, 2023 | 1 | 2023 |
| Exponential increases in high-temperature extremes in North America A Davariashtiyani, M Taherkhani, S Fattahpour, S Vitousek Scientific Reports 13 (1), 19177, 2023 | 1 | 2023 |
| Voxel Image of Crystals for High-Throughput Materials Screening: Formation Energy Prediction by a Deep Convolutional Network A Davariashtiyani, S Kadkhodaei | 1 | 2023 |
| Heat radiation mitigation in rare-earth pyrosilicate composites: A first principles investigation of refractive index mismatch S Kadkhodaei, S Fattahpour, A Davariashtiyani Ceramics International, 2024 | | 2024 |
| Deep Learning for Predicting the Formation Energy and Synthesizability of Crystalline Materials A Davariashtiyani University of Illinois at Chicago, 2023 | | 2023 |
| Phonon-assisted diffusion in bcc phase of titanium from first-principles S Kadkhodaei, A Davariashtiyani arXiv, arXiv: 1910.05806, 2019 | | 2019 |
