Li Li (李力)

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	All	Since 2020
Citations	4059	3505
h-index	19	18
i10-index	24	24

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Kieron BurkeUC Irvine chemistry and physicsVerified email at uci.edu
Patrick RileyRelay TherapeuticsVerified email at relaytx.com
Steven KearnesOpen Reaction DatabaseVerified email at open-reaction-database.org
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Ruoxi SunColumbia University, GoogleVerified email at columbia.edu
Tess SmidtMassachusetts Institute of TechnologyVerified email at mit.edu
Kai KohlhoffStaff Research Scientist, GoogleVerified email at stanford.edu
Lusann YangGoogleVerified email at google.com
Murphy Yuezhen NiuGoogle AI Quantum, University of Maryland, College ParkVerified email at mit.edu
Leslie Vogt-MarantoDepartment of Chemistry (Tuckerman), New York UniversityVerified email at nyu.edu
Ryan PedersonTerray TherapeuticsVerified email at uci.edu
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
John C. SnyderFormer Postdoc, Technische Universität Berlin / Max-Planck-Institut fürVerified email at uci.edu
Zhenpeng ZhouStanford UniversityVerified email at stanford.edu
Richard N. ZareProfessor of Chemistry, Stanford UniversityVerified email at stanford.edu
Minjie FanSoftware Engineer/Researcher, GoogleVerified email at google.com
Thomas E. BakerCanada Research Chair in Quantum Computing for Modelling of Molecules and MaterialsVerified email at uvic.ca
Bhupalee KalitaCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Stefan LeichenauerSandboxAQVerified email at sandboxaq.com
Hanjun DaiGoogle DeepMindVerified email at google.com

Li Li (李力)

Other namesLi Li

Google

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machine learning deep learning differentiable programming symbolic regression density functional theory


Title Sort by citations Sort by year Sort by title	Cited by 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	1087	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	787	2017
Optimization of molecules via deep reinforcement learning Z Zhou, S Kearnes, L Li, RN Zare, P Riley Scientific reports 9 (1), 10752, 2019	696	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	207	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	180	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	152	2016
Quantum circuit optimization with deep reinforcement learning T Fösel, MY Niu, F Marquardt, L Li arXiv preprint arXiv:2103.07585, 2021	148	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	123	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	119	2020
Learning to Approximate Density Functionals B Kalita, L Li, RJ McCarty, K Burke Accounts of Chemical Research 54 (4), 818-826, 2021	90	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	68	2018
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	54	2021
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	46	2010
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	43	2021
Evolving symbolic density functionals H Ma, A Narayanaswamy, P Riley, L Li Science Advances 8 (36), eabq0279, 2022	40	2022
Neural-Guided Symbolic Regression with Asymptotic Constraints L Li, M Fan, R Singh, P Riley arXiv preprint arXiv:1901.07714, 2019	35*	2019
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	30
Energy-based View of Retrosynthesis R Sun, H Dai, L Li, S Kearnes, B Dai arXiv preprint arXiv:2007.13437, 2020	28	2020
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	20	2019
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	19	2018

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