| Gradient based hyperparameter optimization in echo state networks LA Thiede, U Parlitz Neural Networks 115, 23-29, 2019 | 81 | 2019 |
| Analyzing the variety loss in the context of probabilistic trajectory prediction LA Thiede, PP Brahma Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 68 | 2019 |
| Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning LA Thiede, M Krenn, AK Nigam, A Aspuru-Guzik Machine Learning: Science and Technology 3 (3), 035008, 2022 | 49 | 2022 |
| Tartarus: A benchmarking platform for realistic and practical inverse molecular design AK Nigam, R Pollice, G Tom, K Jorner, J Willes, L Thiede, A Kundaje, ... Advances in Neural Information Processing Systems 36, 3263-3306, 2023 | 31 | 2023 |
| In silico chemical experiments in the Age of AI: From quantum chemistry to machine learning and back A Aldossary, JA Campos‐Gonzalez‐Angulo, S Pablo‐García, SX Leong, ... Advanced Materials, 2402369, 2024 | 16 | 2024 |
| Towards equilibrium molecular conformation generation with GFlowNets A Volokhova, M Koziarski, A Hernández-García, CH Liu, S Miret, P Lemos, ... Digital Discovery 3 (5), 1038-1047, 2024 | 12 | 2024 |
| Wasserstein quantum Monte Carlo: a novel approach for solving the quantum many-body Schrödinger equation K Neklyudov, J Nys, L Thiede, J Carrasquilla, Q Liu, M Welling, ... Advances in Neural Information Processing Systems 36, 63461-63482, 2023 | 12 | 2023 |
| Conformer search using SE3-transformers and imitation learning L Thiede, S Miret, K Sadowski, H Xu, M Phielipp, A Aspuru-Guzik AI for Accelerated Materials Design NeurIPS 2022 Workshop, 2022 | 4 | 2022 |
| Sorting out quantum monte carlo J Richter-Powell, L Thiede, A Asparu-Guzik, D Duvenaud arXiv preprint arXiv:2311.05598, 2023 | 3 | 2023 |
| Waveflow: Enforcing boundary conditions in smooth normalizing flows with application to fermionic wave functions L Thiede, C Sun, A Aspuru-Guzik arXiv preprint arXiv:2211.14839, 2022 | 3 | 2022 |
| Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science AH Cheng, CT Ser, M Skreta, A Guzmán-Cordero, L Thiede, A Burger, ... Faraday Discussions 256, 10-60, 2025 | 1 | 2025 |
| Waveflow: Boundary-conditioned normalizing flows applied to fermionic wave functions L Thiede, C Sun, A Aspuru-Guzik APL Machine Learning 2 (4), 2024 | | 2024 |
| How to do impactful research in artificial intelligence for chemistry and materials science A Cheng, C Tian Ser, M Skreta, A Guzmán-Cordero, L Thiede, A Burger, ... arXiv e-prints, arXiv: 2409.10304, 2024 | | 2024 |
| Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning LA Thiede, M Krenn, AK Nigam, A Aspuru-Guzik arXiv preprint arXiv:2012.11293, 2020 | | 2020 |
| Dimension Deficit: Is 3D a Step Too Far for Optimizing Molecules? AG Cordero, L Thiede, G Tom, A Aspuru-Guzik, F Strieth-Kalthoff, ... AI for Accelerated Materials Design-NeurIPS 2024, 0 | | |
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Dirección de correo verificada de utoronto.ca