| 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 | 925 | 2018 |
| Bypassing the Kohn-Sham equations with machine learning F Brockherde, L Vogt, L Li, ME Tuckerman, K Burke, KR Müller Nature Communications 8 (1), 872, 2017 | 722 | 2017 |
| Optimization of molecules via deep reinforcement learning Z Zhou, S Kearnes, L Li, RN Zare, P Riley Scientific reports 9 (1), 10752, 2019 | 612 | 2019 |
| Understanding Machine-learned Density Functionals L Li, JC Snyder, IM Pelaschier, J Huang, UN Niranjan, P Duncan, M Rupp, ... International Journal of Quantum Chemistry 116 (11), 819-833, 2016 | 200 | 2016 |
| 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 | 159 | 2021 |
| Pure density functional for strong correlations and the thermodynamic limit from machine learning L Li, TE Baker, SR White, K Burke Phys. Rev. B 94 (24), 245129, 2016 | 145 | 2016 |
| Quantum circuit optimization with deep reinforcement learning T Fösel, MY Niu, F Marquardt, L Li arXiv preprint arXiv:2103.07585, 2021 | 118 | 2021 |
| Understanding kernel ridge regression: Common behaviors from simple functions to density functionals K Vu, JC Snyder, L Li, M Rupp, BF Chen, T Khelif, KR Müller, K Burke International Journal of Quantum Chemistry 115 (16), 1115-1128, 2015 | 117 | 2015 |
| 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 | 101 | 2020 |
| Learning to Approximate Density Functionals B Kalita, L Li, RJ McCarty, K Burke Accounts of Chemical Research 54 (4), 818-826, 2021 | 73 | 2021 |
| Can exact conditions improve machine-learned density functionals? J Hollingsworth, L Li, TE Baker, K Burke The Journal of chemical physics 148 (24), 2018 | 60 | 2018 |
| Graded index photonic hole: Analytical and rigorous full wave solution S Liu, L Li, Z Lin, HY Chen, J Zi, CT Chan Physical Review B 82 (5), 054204, 2010 | 45 | 2010 |
| Efficient approximation of experimental Gaussian boson sampling B Villalonga, MY Niu, L Li, H Neven, JC Platt, VN Smelyanskiy, S Boixo arXiv preprint arXiv:2109.11525, 2021 | 43 | 2021 |
| Neural-Guided Symbolic Regression with Asymptotic Constraints L Li, M Fan, R Singh, P Riley arXiv preprint arXiv:1901.07714, 2019 | 35* | 2019 |
| Evolving symbolic density functionals H Ma, A Narayanaswamy, P Riley, L Li Science Advances 8 (36), eabq0279, 2022 | 34 | 2022 |
| Towards understanding retrosynthesis by energy-based models R Sun, H Dai, L Li, S Kearnes, B Dai Advances in Neural Information Processing Systems 34, 10186-10194, 2021 | 32 | 2021 |
| Energy-based View of Retrosynthesis R Sun, H Dai, L Li, S Kearnes, B Dai arXiv preprint arXiv:2007.13437, 2020 | 27 | 2020 |
| Tensor field networks: Rotation-and translation-equivariant neural networks for 3D point clouds. arXiv 2018 N Thomas, T Smidt, S Kearnes, L Yang, L Li, K Kohlhoff, P Riley arXiv preprint arXiv:1802.08219 10, 0 | 23 | |
| Decoding Molecular Graph Embeddings with Reinforcement Learning S Kearnes, L Li, P Riley ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data, 2019 | 19 | 2019 |
| Learnability and Complexity of Quantum Samples MY Niu, AM Dai, L Li, A Odena, Z Zhao, V Smelyanskyi, H Neven, S Boixo arXiv preprint arXiv:2010.11983, 2020 | 17 | 2020 |
Kieron BurkeUC Irvine chemistry and physics在 uci.edu 的电子邮件经过验证
