Author

Li Li

Google - Cited by 2,644 - machine learning - deep learning - differentiable programming - symbolic regression - density functional theory

Biography

Li Li has been a researcher at the Google Accelerated Science team since 2017. His research interests include developing algorithms to search solutions in combinatorial spaces of symbolic expressions, molecular graphs and quantum algorithms; hybridizing machine learning and scientific computing using differentiable programming; and other exploratory researches to apply machine learning and large scale computing to physical science problems.
Title
Cited by
Year
Bypassing the Kohn-Sham equations with machine learning
F Brockherde, L Vogt, L Li, ME Tuckerman, K Burke, KR MüllerNature Communications 8 (1), 872, 2017201
622
2017
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 RileyarXiv preprint arXiv:1802.08219, 2018201
614
2018
Optimization of molecules via deep reinforcement learning
Z Zhou, S Kearnes, L Li, RN Zare, P RileyScientific reports 9 (1), 10752, 2019201
455
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, 2016201
167
2016
Pure density functional for strong correlations and the thermodynamic limit from machine learning
L Li, TE Baker, SR White, K BurkePhys. Rev. B 94 (24), 245129, 2016201
112
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 BurkePhysical Review Letters 126 (3), 036401, 2021202
109
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 BurkeInternational Journal of Quantum Chemistry 115 (16), 1115-1128, 2015201
98
2015
Quantum optimization with a novel gibbs objective function and ansatz architecture search
L Li, M Fan, M Coram, P Riley, S LeichenauerPhysical Review Research 2 (2), 023074, 2020202
72
2020
Quantum circuit optimization with deep reinforcement learning
T Fösel, MY Niu, F Marquardt, L LiarXiv preprint arXiv:2103.07585, 2021202
57
2021
Learning to Approximate Density Functionals
B Kalita, L Li, RJ McCarty, K BurkeAccounts of Chemical Research 54 (4), 818-826, 2021202
49
2021
Can exact conditions improve machine-learned density functionals?
J Hollingsworth, L Li, TE Baker, K BurkeThe Journal of chemical physics 148 (24), 2018201
45
2018
Graded index photonic hole: Analytical and rigorous full wave solution
S Liu, L Li, Z Lin, HY Chen, J Zi, CT ChanPhysical Review B 82 (5), 054204, 2010201
43
2010
Neural-Guided Symbolic Regression with Asymptotic Constraints
L Li, M Fan, R Singh, P RileyarXiv preprint arXiv:1901.07714, 201929201
29
2019
Efficient approximation of experimental Gaussian boson sampling
B Villalonga, MY Niu, L Li, H Neven, JC Platt, VN Smelyanskiy, S BoixoarXiv preprint arXiv:2109.11525, 2021202
28
2021
Energy-based View of Retrosynthesis
R Sun, H Dai, L Li, S Kearnes, B DaiarXiv preprint arXiv:07.13437,
20
2020
Evolving symbolic density functionals
H Ma, A Narayanaswamy, P Riley, L LiScience Advances 8 (36), eabq0279, 2022202
17
2022
Decoding Molecular Graph Embeddings with Reinforcement Learning
S Kearnes, L Li, P RileyICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data, 2019201
16
2019
Towards understanding retrosynthesis by energy-based models
R Sun, H Dai, L Li, S Kearnes, B DaiAdvances in Neural Information Processing Systems 34, 10186-10194, 2021202
15
2021
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, 2018201
13
2018
Learnability and Complexity of Quantum Samples
MY Niu, AM Dai, L Li, A Odena, Z Zhao, V Smelyanskyi, H Neven, S BoixoarXiv preprint arXiv:2010.11983, 2020202
13
2020

Referred From: https://scholar.google.com/citations?user=MsImb-AAAAAJ&hl=zh-CN

Cited by
Citations 2644
h-index 16
i10-index 22
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