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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