| Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator Q Li, F Dietrich, EM Bollt, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (10), 2017 | 584 | 2017 |
| Stochastic modified equations and adaptive stochastic gradient algorithms Q Li, C Tai, W E Proceedings of the 34th International Conference on Machine Learning 70 …, 2017 | 395 | 2017 |
| Maximum Principle Based Algorithms for Deep Learning Q Li, L Chen, C Tai, W E Journal of Machine Learning Research 18 (165), 1-29, 2018 | 291 | 2018 |
| An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties Z Ren, SIP Tian, J Noh, F Oviedo, G Xing, J Li, Q Liang, R Zhu, AG Aberle, ... Matter 5 (1), 314-335, 2022 | 271* | 2022 |
| Stochastic modified equations and dynamics of stochastic gradient algorithms i: Mathematical foundations Q Li, C Tai, W E Journal of Machine Learning Research 20 (40), 1-47, 2019 | 240 | 2019 |
| Two-step machine learning enables optimized nanoparticle synthesis F Mekki-Berrada, Z Ren, T Huang, WK Wong, F Zheng, J Xie, IPS Tian, ... npj Computational Materials 7 (1), 55, 2021 | 223 | 2021 |
| A mean-field optimal control formulation of deep learning W E, J Han, Q Li Research in the Mathematical Sciences 6 (1), 1-41, 2019 | 193* | 2019 |
| Deep learning via dynamical systems: An approximation perspective Q Li, T Lin, Z Shen Journal of the European Mathematical Society 25 (5), 1671-1709, 2023 | 159 | 2023 |
| Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics K Hippalgaonkar, Q Li, X Wang, JW Fisher III, J Kirkpatrick, T Buonassisi Nature Reviews Materials 8 (4), 241-260, 2023 | 143 | 2023 |
| An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks Q Li, S Hao Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 110 | 2018 |
| Computing committor functions for the study of rare events using deep learning Q Li, B Lin, W Ren The Journal of Chemical Physics 151 (5), 2019 | 99 | 2019 |
| Machine learning enables polymer cloud-point engineering via inverse design JN Kumar, Q Li, KYT Tang, T Buonassisi, AL Gonzalez-Oyarce, J Ye Npj Computational Materials 5 (1), 73, 2019 | 93 | 2019 |
| Challenges and opportunities of polymer design with machine learning and high throughput experimentation JN Kumar, Q Li, Y Jun Mrs Communications 9 (2), 537-544, 2019 | 76 | 2019 |
| OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle H Yu, X Tian, Q Li arXiv preprint arXiv:2009.02327, 2020 | 70 | 2020 |
| Noisy Hegselmann-Krause systems: phase transition and the 2r-conjecture C Wang, Q Li, W E, B Chazelle Journal of Statistical Physics 166 (5), 1209-1225, 2017 | 65 | 2017 |
| Well-posedness of the limiting equation of a noisy consensus model in opinion dynamics B Chazelle, Q Jiu, Q Li, C Wang Journal of Differential Equations 263 (1), 365-397, 2017 | 62 | 2017 |
| Prediction of interstitial diffusion activation energies of nitrogen, oxygen, boron and carbon in bcc, fcc, and hcp metals using machine learning Y Zeng, Q Li, K Bai Computational Materials Science 144, 232-247, 2018 | 61 | 2018 |
| Multi‐fidelity high‐throughput optimization of electrical conductivity in P3HT‐CNT composites D Bash, Y Cai, V Chellappan, SL Wong, X Yang, P Kumar, JD Tan, ... Advanced Functional Materials 31 (36), 2102606, 2021 | 55 | 2021 |
| Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics Z Ren, F Oviedo, M Thway, SIP Tian, Y Wang, H Xue, JD Perea, ... npj Computational Materials 6 (1), 1-9, 2020 | 55* | 2020 |
| On matching, and even rectifying, dynamical systems through Koopman operator eigenfunctions EM Bollt, Q Li, F Dietrich, I Kevrekidis SIAM Journal on Applied Dynamical Systems 17 (2), 1925-1960, 2018 | 53 | 2018 |
Tonio BuonassisiMassachusetts Institute of TechnologyVerified email at mit.edu
