Li Li, Ph.D.
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 TheoryGoogle:
"https://scholar.google.com/citations?user=MsImb-AAAAAJ&hl=en&oi=sra"Mean distance: (not calculated yet)
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Publications
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| Ma H, Narayanaswamy A, Riley P, et al. (2022) Evolving symbolic density functionals. Science Advances. 8: eabq0279 |
| Kalita B, Pederson R, Chen J, et al. (2022) How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems? The Journal of Physical Chemistry Letters. 13: 2540-2547 |
| Li L, Hoyer S, Pederson R, et al. (2021) Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics. Physical Review Letters. 126: 036401 |
| Kalita B, Li L, McCarty RJ, et al. (2021) Learning to Approximate Density Functionals. Accounts of Chemical Research |
| Zhou Z, Kearnes S, Li L, et al. (2020) Author Correction: Optimization of Molecules via Deep Reinforcement Learning. Scientific Reports. 10: 10478 |
| Zhou Z, Kearnes S, Li L, et al. (2019) Optimization of Molecules via Deep Reinforcement Learning. Scientific Reports. 9: 10752 |
| Hollingsworth J, Li L, Baker TE, et al. (2018) Can exact conditions improve machine-learned density functionals? The Journal of Chemical Physics. 148: 241743 |
| Brockherde F, Vogt L, Li L, et al. (2017) Bypassing the Kohn-Sham equations with machine learning. Nature Communications. 8: 872 |
| Yan Z, Liu W, Zhang C, et al. (2016) Quantitative correlation between facets defects of RDX crystals and their laser sensitivity. Journal of Hazardous Materials. 313: 103-111 |
| Li L, Baker TE, White SR, et al. (2016) Pure density functional for strong correlation and the thermodynamic limit from machine learning Physical Review B. 94: 245129 |