| Machine-learning prediction of CO adsorption in thiolated, Ag-alloyed Au nanoclusters G Panapitiya, G Avendaño-Franco, P Ren, X Wen, Y Li, JP Lewis Journal of the American Chemical Society 140 (50), 17508-17514, 2018 | 127 | 2018 |
| Controlling Ag-doping in [Ag x Au 25− x (SC 6 H 11) 18]− nanoclusters: cryogenic optical, electronic and electrocatalytic properties R Jin, S Zhao, C Liu, M Zhou, G Panapitiya, Y Xing, NL Rosi, JP Lewis, ... Nanoscale 9 (48), 19183-19190, 2017 | 46 | 2017 |
| Evaluation of deep learning architectures for aqueous solubility prediction G Panapitiya, M Girard, A Hollas, J Sepulveda, V Murugesan, W Wang, ... ACS omega 7 (18), 15695-15710, 2022 | 35 | 2022 |
| Structural and catalytic properties of the Au25-xAgx (SCH3) 18 (x= 6, 7, 8) Nanocluster G Panapitiya, H Wang, Y Chen, E Hussain, R Jin, JP Lewis Physical Chemistry Chemical Physics, 2018 | 21 | 2018 |
| Slow Relaxation of Surface Plasmon Excitations in Au55: The Key to Efficient Plasmonic Heating in Au/TiO2 O Ranasingha, H Wang, V Zobač, P Jelínek, G Panapitiya, AJ Neukirch, ... The Journal of Physical Chemistry Letters 7 (8), 1563-1569, 2016 | 16 | 2016 |
| Optical absorption and disorder in delafossites TR Senty, B Haycock, J Lekse, C Matranga, H Wang, G Panapitiya, ... Applied Physics Letters 111 (1), 2017 | 14 | 2017 |
| SOMAS: a platform for data-driven material discovery in redox flow battery development P Gao, A Andersen, J Sepulveda, GU Panapitiya, A Hollas, EG Saldanha, ... Scientific Data 9 (1), 740, 2022 | 11 | 2022 |
| Predicting aqueous solubility of organic molecules using deep learning models with varied molecular representations G Panapitiya, M Girard, A Hollas, V Murugesan, W Wang, E Saldanha arXiv preprint arXiv:2105.12638, 2021 | 7 | 2021 |
| Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction T Yin, G Panapitiya, ED Coda, EG Saldanha Journal of Cheminformatics 15 (1), 105, 2023 | 3 | 2023 |
| Structural and electronic properties of Fe-doped silver delafossites: AgAl1− xFexO2 and AgGa1− xFexO2 (x= 1–5%) G Panapitiya, G Avendaño-Franco, JP Lewis Computational Materials Science 170, 109173, 2019 | 3 | 2019 |
| Extracting Material Property Measurement Data from Scientific Articles G Panapitiya, F Parks, J Sepulveda, E Saldanha The 2021 Conference on Empirical Methods in Natural Language Processing …, 2021 | 2 | 2021 |
| Impacts of Data and Models on Unsupervised Pre-training for Molecular Property Prediction E Coda, GU Panapitiya, E Saldanha AI for Accelerated Materials Design-NeurIPS 2023 Workshop, 2023 | | 2023 |
| Impact of Molecular Representations on Deep Learning Model Comparisons in Drug Response Predictions GU Panapitiya, C Knutson, AD McNaughton, R Jain, J Wozniak, T Brettin, ... | | 2023 |
| Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models G Panapitiya, E Saldanha arXiv preprint arXiv:2302.06454, 2023 | | 2023 |
| Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction C Knutson, G Panapitiya, R Varikoti, N Kumar arXiv preprint arXiv:2212.09991, 2022 | | 2022 |
| MRWFD Kr Capture Project M Greenhalgh, AK Welty, MS Fujimoto, TG Garn, P Thallapally, P Gao, ... Idaho National Laboratory (INL), Idaho Falls, ID (United States), 2022 | | 2022 |
| Aqueous Solubility Prediction Using Deep Learning Models with Different Molecular Representations G Panapitiya, E Saldanha Mechanistic Machine Learning and Digital Twins for Computational Science …, 2021 | | 2021 |
| Novel Computational Methods for Catalytic Applications GU Panapitiya | | 2019 |
| Machine-learning model to predict adsorption energies in thiolated bimetallic nanoclusters G Panapitiya, G Frano, J Lewis APS March Meeting Abstracts 2019, A18. 008, 2019 | | 2019 |
| Optical Absorption and Carrier Dynamics of Semiconductor Delafossites. R Sooriyagoda, TR Senty, B Haycock, J Lekse, C Matranga, H Wang, ... Bulletin of the American Physical Society 61, 2016 | | 2016 |
