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Li Li (李力)
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Cited by
Year
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
7912018
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
6762017
Optimization of molecules via deep reinforcement learning
Z Zhou, S Kearnes, L Li, RN Zare, P Riley
Scientific reports 9 (1), 10752, 2019
5442019
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
1862016
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
1352021
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
1162016
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
1132015
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
912020
Quantum circuit optimization with deep reinforcement learning
T Fösel, MY Niu, F Marquardt, L Li
arXiv preprint arXiv:2103.07585, 2021
802021
Learning to Approximate Density Functionals
B Kalita, L Li, RJ McCarty, K Burke
Accounts of Chemical Research 54 (4), 818-826, 2021
602021
Can exact conditions improve machine-learned density functionals?
J Hollingsworth, L Li, TE Baker, K Burke
The Journal of chemical physics 148 (24), 2018
562018
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
452010
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
382021
Neural-Guided Symbolic Regression with Asymptotic Constraints
L Li, M Fan, R Singh, P Riley
arXiv preprint arXiv:1901.07714, 2019
32*2019
Evolving symbolic density functionals
H Ma, A Narayanaswamy, P Riley, L Li
Science Advances 8 (36), eabq0279, 2022
272022
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
272021
Energy-based View of Retrosynthesis
R Sun, H Dai, L Li, S Kearnes, B Dai
arXiv preprint arXiv:2007.13437, 2020
252020
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
182019
Efficient prediction of 3D electron densities using machine learning
M Bogojeski, F Brockherde, L Vogt-Maranto, L Li, ME Tuckerman, K Burke, ...
NeurIPS 2018 Workshop on Machine Learning for Molecules and Materials, 2018
152018
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
142020
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Articles 1–20