| TorchANI: A free and open source PyTorch-based deep learning implementation of the ANI neural network potentials X Gao, F Ramezanghorbani, O Isayev, JS Smith, AE Roitberg Journal of chemical information and modeling 60 (7), 3408-3415, 2020 | 205 | 2020 |
| Transferable neural network potential energy surfaces for closed-shell organic molecules: Extension to ions LD Jacobson, JM Stevenson, F Ramezanghorbani, D Ghoreishi, ... Journal of Chemical Theory and Computation 18 (4), 2354-2366, 2022 | 33 | 2022 |
| High-dimensional neural network potential for liquid electrolyte simulations S Dajnowicz, G Agarwal, JM Stevenson, LD Jacobson, ... The Journal of Physical Chemistry B 126 (33), 6271-6280, 2022 | 28 | 2022 |
| Quantum chemistry common driver and databases (QCDB) and quantum chemistry engine (QCEngine): Automation and interoperability among computational chemistry programs DGA Smith, AT Lolinco, ZL Glick, J Lee, A Alenaizan, TA Barnes, ... The Journal of chemical physics 155 (20), 2021 | 26 | 2021 |
| Optimizing protein–polymer interactions in a poly (ethylene glycol) coarse-grained model F Ramezanghorbani, P Lin, CM Colina The Journal of Physical Chemistry B 122 (33), 7997-8005, 2018 | 22 | 2018 |
| A multi-state coarse grained modeling approach for an intrinsically disordered peptide F Ramezanghorbani, C Dalgicdir, M Sayar The Journal of Chemical Physics 147 (9), 2017 | 7 | 2017 |
| Leveraging multitask learning to improve the transferability of machine learned force fields L Jacobson, J Stevenson, F Ramezanghorbani, S Dajnowicz, K Leswing | 4 | 2023 |
| Developing Machine Learning Models to Enhance Applicability of Neural Network Potentials in Drug Discovery F Ramezanghorbani University of Florida, 2020 | 3 | 2020 |
| A transferable coarse-grained model for peptides that display an environment driven conformational transition F Ramezanghorbani Koç University, 2015 | 1 | 2015 |
| Efficient exploration of the conformational space for PEGylated proteins through biased coarse-grained molecular dynamics F Ramezanghorbani, P Lin, C Colina ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
| Representing the Marginal Stability of Peptides in Coarse Grained Models M Sayar, C Dalgicdir, F Ramezanghorbani APS March Meeting Abstracts 2017, R4. 008, 2017 | | 2017 |
| Ultra-coarse-grained models for gel-forming mucins P Lin, F Ramezanghorbani, C Colina ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 252, 2016 | | 2016 |
| Latent space representation learning as an auxiliary task for training neural network potentials F Ramezanghorbani, AE Roitberg | | |
Ping LinScientist, University of Floridachem.ufl.edu üzerinde doğrulanmış e-posta adresine sahip
