Li Li (李力)
TitleCited byYear
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
1712017
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
662016
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
572016
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
452015
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
282010
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
262018
Optimization of molecules via deep reinforcement learning
Z Zhou, S Kearnes, L Li, RN Zare, P Riley
Scientific Reports 9 (1), 10752, 2019
92019
Can exact conditions improve machine-learned density functionals?
J Hollingsworth, L Li, TE Baker, K Burke
The Journal of Chemical Physics 148 (24), 241743, 2018
62018
Improving Malware Detection Accuracy by Extracting Icon Information
S Pedro, AM Sepehr, L Li
IEEE International Conference on Multimedia Information Processing and Retrieval, 2018
2*2018
Lazy stochastic principal component analysis
M Wojnowicz, D Nguyen, L Li, X Zhao
IEEE International Conference on Data Mining Workshop, 2017
22017
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
12019
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
12018
Neural-Guided Symbolic Regression with Semantic Prior
L Li, M Fan, R Singh, P Riley
arXiv preprint arXiv:1901.07714, 2019
2019
Protecting devices from malicious files based on n-gram processing of sequential data
L Ll, X Zhao, S Akhavan-Masouleh, JH Brock, Y Oliinyk, M Wolff
US Patent App. 15/490,797, 2018
2018
Recent developments in density functional approximations
L Li, K Burke
Handbook of Materials Modeling. Volume 1 Methods: Theory and Modeling 1, 2018
2018
IPAM Program on Machine Learning & Many-Particle Systems - Recent Progress and Open Problems
http://www.ipam.ucla.edu/wp-content/uploads/2017/03/Understanding-Many …, 2017
2017
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Articles 1–16