| Structural Deformation Controls Charge Losses in MAPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics G Zhou, W Chu, OV Prezhdo ACS Energy Letters 5 (6), 1930-1938, 2020 | 62 | 2020 |
| Common defects accelerate charge separation and reduce recombination in CNT/molecule composites: atomistic quantum dynamics R Sarkar, M Kar, M Habib, G Zhou, T Frauenheim, P Sarkar, S Pal, ... Journal of the American Chemical Society 143 (17), 6649-6656, 2021 | 44 | 2021 |
| Core-dependent properties of copper nanoclusters: valence-pure nanoclusters as NIR TADF emitters and mixed-valence ones as semiconductors LLM Zhang, G Zhou, G Zhou, HK Lee, N Zhao, OV Prezhdo, TCW Mak Chemical science 10 (43), 10122-10128, 2019 | 44 | 2019 |
| Machine learned Hückel theory: Interfacing physics and deep neural networks T Zubatiuk, B Nebgen, N Lubbers, JS Smith, R Zubatyuk, G Zhou, C Koh, ... The Journal of Chemical Physics 154 (24), 2021 | 40 | 2021 |
| Modeling Auger Processes with Nonadiabatic Molecular Dynamics G Zhou, G Lu, OV Prezhdo Nano Letters 21 (1), 756-761, 2020 | 31 | 2020 |
| Graphics processing unit-accelerated semiempirical Born Oppenheimer molecular dynamics using PyTorch G Zhou, B Nebgen, N Lubbers, W Malone, AMN Niklasson, S Tretiak Journal of Chemical Theory and Computation 16 (8), 4951-4962, 2020 | 31 | 2020 |
| Dependence between Structural and Electronic Properties of CsPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics SM Mangan, G Zhou, W Chu, OV Prezhdo The Journal of Physical Chemistry Letters 12 (35), 8672-8678, 2021 | 29 | 2021 |
| Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics G Zhou, N Lubbers, K Barros, S Tretiak, B Nebgen Proceedings of the National Academy of Sciences 119 (27), e2120333119, 2022 | 28 | 2022 |
| Molecular Simulation of MoS2 Exfoliation G Zhou, P Rajak, S Susarla, PM Ajayan, RK Kalia, A Nakano, P Vashishta Scientific reports 8 (1), 16761, 2018 | 28 | 2018 |
| Electron–phonon scattering is much weaker in carbon nanotubes than in graphene nanoribbons G Zhou, C Cen, S Wang, M Deng, OV Prezhdo The Journal of Physical Chemistry Letters 10 (22), 7179-7187, 2019 | 25 | 2019 |
| Chemically Switchable n-Type and p-Type Conduction in Bismuth Selenide Nanoribbons for Thermoelectric Energy Harvesting Y Xiong*, G Zhou*, NC Lai, X Wang, YC Lu, OV Prezhdo, D Xu ACS nano 15 (2), 2791–2799, 2021 | 17 | 2021 |
| Ultrafast Electronic Relaxation Dynamics of Atomically Thin MoS2 Is Accelerated by Wrinkling C Xu, G Zhou, EM Alexeev, AR Cadore, I Paradisanos, AK Ott, G Soavi, ... Acs Nano 17 (17), 16682-16694, 2023 | 11 | 2023 |
| Semi-empirical shadow molecular dynamics: A pytorch implementation M Kulichenko, K Barros, N Lubbers, N Fedik, G Zhou, S Tretiak, B Nebgen, ... Journal of Chemical Theory and Computation 19 (11), 3209-3222, 2023 | 2 | 2023 |
| PINDER: The protein interaction dataset and evaluation resource D Kovtun, M Akdel, A Goncearenco, G Zhou, G Holt, D Baugher, D Lin, ... bioRxiv, 2024.07. 17.603980, 2024 | | 2024 |
| PLINDER: The protein-ligand interactions dataset and evaluation resource J Durairaj, Y Adeshina, Z Cao, X Zhang, V Oleinikovas, T Duignan, ... bioRxiv, 2024.07. 17.603955, 2024 | | 2024 |
| Theoretical Modeling of Nanoscale Systems: Applications and Method Development G Zhou UNIVERSITY OF SOUTHERN CALIFORNIA, 2021 | | 2021 |
| Machine Learning with Domain Knowledge of Quantum Chemistry G Zhou, BT Nebgen, NE Lubbers, S Tretiak Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2020 | | 2020 |
| Molecular Simulations of Shear Exfoliation of MoS 2 G Zhou, R Kalia, A Nakano, P Vashishta APS March Meeting Abstracts 2018, A36. 009, 2018 | | 2018 |
| Molecular Simulation of MoS\textbf{ Exfoliation} G Zhou, R Kalia, A Nakano, P Vashishta Bulletin of the American Physical Society 62, 2017 | | 2017 |
Oleg PrezhdoProfessor of Chemistry, and Physics and Astronomy, University of Southern California在 usc.edu 的电子邮件经过验证
