| Fidelity, dynamic structure factor, and susceptibility in critical phenomena WL You, YW Li, SJ Gu Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 76 (2 …, 2007 | 589 | 2007 |
| QMCPACK: an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids J Kim, AD Baczewski, TD Beaudet, A Benali, MC Bennett, MA Berrill, ... Journal of Physics: Condensed Matter 30 (19), 195901, 2018 | 334 | 2018 |
| Generic, hierarchical framework for massively parallel Wang-Landau sampling T Vogel, YW Li, T Wüst, DP Landau Physical review letters 110 (21), 210603, 2013 | 190 | 2013 |
| Extending machine learning beyond interatomic potentials for predicting molecular properties N Fedik, R Zubatyuk, M Kulichenko, N Lubbers, JS Smith, B Nebgen, ... Nature Reviews Chemistry 6 (9), 653-672, 2022 | 103 | 2022 |
| Scalable replica-exchange framework for Wang-Landau sampling T Vogel, YW Li, T Wüst, DP Landau Physical Review E 90 (2), 023302, 2014 | 100 | 2014 |
| Machine-learning-assisted insight into spin ice Dy2Ti2O7 AM Samarakoon, K Barros, YW Li, M Eisenbach, Q Zhang, F Ye, ... Nature communications 11 (1), 892, 2020 | 96 | 2020 |
| Towards an accurate description of perovskite ferroelectrics: exchange and correlation effects SF Yuk, KC Pitike, SM Nakhmanson, M Eisenbach, YW Li, VR Cooper Scientific reports 7 (1), 43482, 2017 | 90 | 2017 |
| The rise of neural networks for materials and chemical dynamics M Kulichenko, JS Smith, B Nebgen, YW Li, N Fedik, AI Boldyrev, ... The Journal of Physical Chemistry Letters 12 (26), 6227-6243, 2021 | 76 | 2021 |
| Uncertainty-driven dynamics for active learning of interatomic potentials M Kulichenko, K Barros, N Lubbers, YW Li, R Messerly, S Tretiak, ... Nature Computational Science 3 (3), 230-239, 2023 | 73 | 2023 |
| Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation system S Das, P Motamarri, V Gavini, B Turcksin, YW Li, B Leback Proceedings of the international conference for high performance computing …, 2019 | 62 | 2019 |
| Generic folding and transition hierarchies for surface adsorption of hydrophobic-polar lattice model proteins YW Li, T Wüst, DP Landau Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 87 (1 …, 2013 | 50 | 2013 |
| Unraveling the beautiful complexity of simple lattice model polymers and proteins using Wang-Landau sampling T Wüst, YW Li, DP Landau Journal of Statistical Physics 144 (3), 638-651, 2011 | 50 | 2011 |
| A new paradigm for petascale Monte Carlo simulation: Replica exchange Wang-Landau sampling YW Li, T Vogel, T Wüst, DP Landau Journal of Physics: Conference Series 510 (1), 012012, 2014 | 43 | 2014 |
| Monte Carlo simulations of the HP model (the “Ising model” of protein folding) YW Li, T Wüst, DP Landau Computer physics communications 182 (9), 1896-1899, 2011 | 40 | 2011 |
| Effect of single-site mutations on hydrophobic-polar lattice proteins G Shi, T Vogel, T Wüst, YW Li, DP Landau Physical Review E 90 (3), 033307, 2014 | 28 | 2014 |
| Exploring new frontiers in statistical physics with a new, parallel Wang-Landau framework T Vogel, YW Li, T Wüst, DP Landau Journal of Physics: Conference Series 487 (1), 012001, 2014 | 24 | 2014 |
| Numerical integration using Wang–Landau sampling YW Li, T Wüst, DP Landau, HQ Lin Computer physics communications 177 (6), 524-529, 2007 | 24 | 2007 |
| Pre-exascale accelerated application development: The ORNL Summit experience L Luo, TP Straatsma, LEA Suarez, R Broer, D Bykov, EF D'Azevedo, ... IBM Journal of Research and Development 64 (3/4), 11: 1-11: 21, 2020 | 23 | 2020 |
| Multiscale simulation of plasma flows using active learning A Diaw, K Barros, J Haack, C Junghans, B Keenan, YW Li, D Livescu, ... Physical Review E 102 (2), 023310, 2020 | 20 | 2020 |
| Fast and stable predictions of total energy of solid solution alloys M Lupo Pasini, YW Li, J Yin, J Zhang, K Barros, M Eisenbach arXiv e-prints, arXiv: 1912.11152, 2019 | 19* | 2019 |