| Neural message passing for quantum chemistry J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl International conference on machine learning, 1263-1272, 2017 | 9131 | 2017 |
| Molecular graph convolutions: moving beyond fingerprints S Kearnes, K McCloskey, M Berndl, V Pande, P Riley Journal of computer-aided molecular design 30, 595-608, 2016 | 1826 | 2016 |
| Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds N Thomas, T Smidt, S Kearnes, L Yang, L Li, K Kohlhoff, P Riley arXiv preprint arXiv:1802.08219, 2018 | 1027 | 2018 |
| Prediction errors of molecular machine learning models lower than hybrid DFT error FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ... Journal of chemical theory and computation 13 (11), 5255-5264, 2017 | 713* | 2017 |
| Optimization of molecules via deep reinforcement learning Z Zhou, S Kearnes, L Li, RN Zare, P Riley Scientific reports 9 (1), 10752, 2019 | 670 | 2019 |
| Massively multitask networks for drug discovery B Ramsundar, S Kearnes, P Riley, D Webster, D Konerding, V Pande arXiv preprint arXiv:1502.02072, 2015 | 616 | 2015 |
| Deep diversification of an AAV capsid protein by machine learning DH Bryant, A Bashir, S Sinai, NK Jain, PJ Ogden, PF Riley, GM Church, ... Nature Biotechnology 39 (6), 691-696, 2021 | 274 | 2021 |
| A Bayesian experimental autonomous researcher for mechanical design AE Gongora, B Xu, W Perry, C Okoye, P Riley, KG Reyes, EF Morgan, ... Science advances 6 (15), eaaz1708, 2020 | 232 | 2020 |
| Kohn-Sham equations as regularizer: Building prior knowledge into machine-learned physics L Li, S Hoyer, R Pederson, R Sun, ED Cubuk, P Riley, K Burke Physical review letters 126 (3), 036401, 2021 | 173 | 2021 |
| Three pitfalls to avoid in machine learning P Riley Nature 572 (7767), 27-29, 2019 | 144 | 2019 |
| Machine learning on DNA-encoded libraries: a new paradigm for hit finding K McCloskey, EA Sigel, S Kearnes, L Xue, X Tian, D Moccia, D Gikunju, ... Journal of Medicinal Chemistry 63 (16), 8857-8866, 2020 | 142 | 2020 |
| Spades-a distributed agent simulation environment with software-in-the-loop execution Riley, Riley Proceedings of the 2003 Winter Simulation Conference, 2003. 1, 817-825 Vol. 1, 2003 | 136 | 2003 |
| The CMUnited-99 champion simulator team P Stone, P Riley, M Veloso RoboCup-99: Robot Soccer World Cup III 3, 35-48, 2000 | 135 | 2000 |
| International conference on machine learning J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl Neural message passing for quantum chemistry, 2017 | 127 | 2017 |
| Quantum optimization with a novel Gibbs objective function and ansatz architecture search L Li, M Fan, M Coram, P Riley, S Leichenauer Physical Review Research 2 (2), 023074, 2020 | 115 | 2020 |
| The CMUnited-98 champion simulator team P Stone, M Veloso, P Riley Robot Soccer World Cup, 61-76, 1998 | 115 | 1998 |
| Message passing neural networks J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl Machine learning meets quantum physics, 199-214, 2020 | 107 | 2020 |
| On behavior classification in adversarial environments P Riley, M Veloso Distributed autonomous robotic systems 4, 371-380, 2000 | 106 | 2000 |
| Defining and using ideal teammate and opponent agent models P Stone, P Riley, M Veloso AAAI/IAAI, 1040-1045, 2000 | 87 | 2000 |
| Planning for Distributed Execution through Use of Probabilistic Opponent Models. P Riley, MM Veloso AIPS 2001, 72-82, 2002 | 86 | 2002 |