Developing grid-based smart proxy model to evaluate various water flooding injection scenarios Y Haghshenas, M Emami Niri, S Amini, R Amiri Kolajoobi Petroleum Science and Technology 38 (17), 870-881, 2020 | 16 | 2020 |
A physically-supported data-driven proxy modeling based on machine learning classification methods: Application to water front movement prediction Y Haghshenas, ME Niri, S Amini, RA Kolajoobi Journal of Petroleum Science and Engineering 196, 107828, 2021 | 11 | 2021 |
Enhancing prediction accuracy of physical band gaps in semiconductor materials H Masood, T Sirojan, CY Toe, PV Kumar, Y Haghshenas, PHL Sit, R Amal, ... Cell Reports Physical Science 4 (9), 2023 | 3 | 2023 |
Predicting the rates of photocatalytic hydrogen evolution over cocatalyst-deposited TiO 2 using machine learning with active photon flux as a unifying feature Y Haghshenas, WP Wong, D Gunawan, A Khataee, R Keyikoğlu, ... EES Catalysis 2 (2), 612-623, 2024 | 2 | 2024 |
A Data-Driven proxy modeling approach adapted to well placement optimization problem R Amiri Kolajoobi, M Emami Niri, S Amini, Y Haghshenas Journal of Energy Resources Technology 145 (1), 013401, 2023 | 2 | 2023 |
Full prediction of band potentials in semiconductor materials Y Haghshenas, WP Wong, V Sethu, R Amal, PV Kumar, WY Teoh Materials Today Physics 46, 101519, 2024 | | 2024 |
Rational Design of Earth‐Abundant Catalysts toward Sustainability J Guo, Y Haghshenas, Y Jiao, P Kumar, BI Yakobson, A Roy, Y Jiao, ... Advanced Materials, 2407102, 2024 | | 2024 |