| Deep learning for chemical reaction prediction D Fooshee, A Mood, E Gutman, M Tavakoli, G Urban, F Liu, N Huynh, ... Molecular Systems Design & Engineering 3 (3), 442-452, 2018 | 196 | 2018 |
| Splash: Learnable activation functions for improving accuracy and adversarial robustness M Tavakoli, F Agostinelli, P Baldi Neural Networks 140, 1-12, 2021 | 39 | 2021 |
| Quantum mechanics and machine learning synergies: graph attention neural networks to predict chemical reactivity M Tavakoli, A Mood, D Van Vranken, P Baldi Journal of Chemical Information and Modeling 62 (9), 2121-2132, 2022 | 26 | 2022 |
| Methyl anion affinities of the canonical organic functional groups A Mood, M Tavakoli, E Gutman, D Kadish, P Baldi, DL Van Vranken The Journal of organic chemistry 85 (6), 4096-4102, 2020 | 26 | 2020 |
| Continuous Representation of Molecules Using Graph Variational Autoencoder M Tavakoli, P Baldi AAAI Spring Symposium: MLPS, 2020 | 16 | 2020 |
| Methyl cation affinities of canonical organic functional groups D Kadish, AD Mood, M Tavakoli, ES Gutman, P Baldi, DL Van Vranken The Journal of Organic Chemistry 86 (5), 3721-3729, 2021 | 14 | 2021 |
| Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation M Tavakoli, A Shmakov, F Ceccarelli, P Baldi AAAI 2022, Deep Learning on Graphs: Methods and Applications, 2022 | 9 | 2022 |
| Rmechdb: A public database of elementary radical reaction steps M Tavakoli, YTT Chiu, P Baldi, AM Carlton, D Van Vranken Journal of chemical information and modeling 63 (4), 1114-1123, 2023 | 7 | 2023 |
| Deep learning models of the discrete component of the Galactic interstellar -ray emission A Shmakov, M Tavakoli, P Baldi, CM Karwin, A Broughton, S Murgia Physical Review D 107 (6), 063018, 2023 | 3 | 2023 |
| AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning M Tavakoli, P Baldi, AM Carlton, YT Chiu, A Shmakov, D Van Vranken Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Improved modeling of the discrete component of the Galactic interstellar -ray emission and implications for the Fermi–LAT Galactic Center excess CM Karwin, A Broughton, S Murgia, A Shmakov, M Tavakoli, P Baldi Physical Review D 107 (12), 123032, 2023 | 2 | 2023 |
| PMechDB: A Public Database of Elementary Polar Reaction Steps M Tavakoli, RJ Miller, MC Angel, MA Pfeiffer, ES Gutman, AD Mood, ... Journal of Chemical Information and Modeling, 2024 | 1 | 2024 |
| Tourbillon: a physically plausible neural architecture M Tavakoli, P Sadowski, P Baldi arXiv preprint arXiv:2107.06424, 2021 | 1 | 2021 |
| Unraveling the Molecular Magic: AI Insights on the Formation of Extraordinarily Stretchable Hydrogels SH Emmami, AP Meibody, L Tayebi, M Tavakoli, P Baldi arXiv preprint arXiv:2403.05129, 2024 | | 2024 |
| Machine Intelligence for Chemistry: From Deep Learning Architectures to Open Data M Tavakoli University of California, Irvine, 2023 | | 2023 |
| Deep Learning to Reconstruct Gas Skymaps for Dark Matter Detection A Shmakov, T Mohammdamin, PB Chris Karwin, Simona Murgia Neural Information Processing Systems 2020, Machine Learning and the …, 2020 | | 2020 |
Pierre BaldiProfessor, University of California, IrvineVerified email at ics.uci.edu
