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Li Li, Ph.D. - Publications

Affiliations: 
2013-2016 Physics University of California, Irvine, Irvine, CA 
 2017- Google Accelerated Science Google, Inc., Mountain View, CA, United States 
Area:
Machine Learning, Density Functional Theory
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13 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2022 Ma H, Narayanaswamy A, Riley P, Li L. Evolving symbolic density functionals. Science Advances. 8: eabq0279. PMID 36083906 DOI: 10.1126/sciadv.abq0279  0.348
2022 Kalita B, Pederson R, Chen J, Li L, Burke K. How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems? The Journal of Physical Chemistry Letters. 13: 2540-2547. PMID 35285630 DOI: 10.1021/acs.jpclett.2c00371  0.657
2021 Li L, Hoyer S, Pederson R, Sun R, Cubuk ED, Riley P, Burke K. Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics. Physical Review Letters. 126: 036401. PMID 33543980 DOI: 10.1103/PhysRevLett.126.036401  0.568
2021 Kalita B, Li L, McCarty RJ, Burke K. Learning to Approximate Density Functionals. Accounts of Chemical Research. PMID 33534553 DOI: 10.1021/acs.accounts.0c00742  0.609
2020 Zhou Z, Kearnes S, Li L, Zare RN, Riley P. Author Correction: Optimization of Molecules via Deep Reinforcement Learning. Scientific Reports. 10: 10478. PMID 32572065 DOI: 10.1038/S41598-020-66840-X  0.309
2019 Zhou Z, Kearnes S, Li L, Zare RN, Riley P. Optimization of Molecules via Deep Reinforcement Learning. Scientific Reports. 9: 10752. PMID 31341196 DOI: 10.1038/S41598-019-47148-X  0.322
2018 Hollingsworth J, Li L, Baker TE, Burke K. Can exact conditions improve machine-learned density functionals? The Journal of Chemical Physics. 148: 241743. PMID 29960336 DOI: 10.1063/1.5025668  0.615
2017 Brockherde F, Vogt L, Li L, Tuckerman ME, Burke K, Müller KR. Bypassing the Kohn-Sham equations with machine learning. Nature Communications. 8: 872. PMID 29021555 DOI: 10.1038/S41467-017-00839-3  0.649
2016 Yan Z, Liu W, Zhang C, Wang X, Li J, Yang Z, Xiang X, Huang M, Tan B, Zhou G, Liao W, Li Z, Li L, Yan H, Yuan X, et al. Quantitative correlation between facets defects of RDX crystals and their laser sensitivity. Journal of Hazardous Materials. 313: 103-111. PMID 27054669 DOI: 10.1016/j.jhazmat.2016.03.071  0.335
2016 Li L, Baker TE, White SR, Burke K. Pure density functional for strong correlation and the thermodynamic limit from machine learning Physical Review B. 94: 245129. DOI: 10.1103/Physrevb.94.245129  0.648
2015 Yin A, Zhang Q, Kong X, Jia L, Yang Z, Meng L, Li L, Wang X, Qiao Y, Lu N, Yang Q, Shen K, Kong B. JAM3 methylation status as a biomarker for diagnosis of preneoplastic and neoplastic lesions of the cervix. Oncotarget. PMID 26517242 DOI: 10.18632/oncotarget.6250  0.344
2015 Li L, Snyder JC, Pelaschier IM, Huang J, Niranjan UN, Duncan P, Rupp M, Müller KR, Burke K. Understanding machine-learned density functionals International Journal of Quantum Chemistry. DOI: 10.1002/Qua.25040  0.655
2015 Vu K, Snyder JC, Li L, Rupp M, Chen BF, Khelif T, Müller KR, Burke K. Understanding kernel ridge regression: Common behaviors from simple functions to density functionals International Journal of Quantum Chemistry. 115: 1115-1128. DOI: 10.1002/Qua.24939  0.622
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