| The effective field theory of dark matter direct detection AL Fitzpatrick, W Haxton, E Katz, N Lubbers, Y Xu Journal of Cosmology and Astroparticle Physics 2013 (02), 004, 2013 | 689 | 2013 |
| Less is more: Sampling chemical space with active learning JS Smith, B Nebgen, N Lubbers, O Isayev, AE Roitberg The Journal of chemical physics 148 (24), 2018 | 664 | 2018 |
| Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning JS Smith, BT Nebgen, R Zubatyuk, N Lubbers, C Devereux, K Barros, ... Nature communications 10 (1), 2903, 2019 | 613 | 2019 |
| Machine learning predicts laboratory earthquakes B Rouet‐Leduc, C Hulbert, N Lubbers, K Barros, CJ Humphreys, ... Geophysical Research Letters 44 (18), 9276-9282, 2017 | 419 | 2017 |
| Hierarchical modeling of molecular energies using a deep neural network N Lubbers, JS Smith, K Barros The Journal of chemical physics 148 (24), 2018 | 314 | 2018 |
| The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules JS Smith, R Zubatyuk, B Nebgen, N Lubbers, K Barros, AE Roitberg, ... Scientific data 7 (1), 134, 2020 | 188 | 2020 |
| Inferring low-dimensional microstructure representations using convolutional neural networks N Lubbers, T Lookman, K Barros Physical Review E 96 (5), 052111, 2017 | 138 | 2017 |
| Embedding hard physical constraints in neural network coarse-graining of 3D turbulence AT Mohan, N Lubbers, D Livescu, M Chertkov arXiv preprint arXiv:2002.00021, 2020 | 129* | 2020 |
| Discovering a transferable charge assignment model using machine learning AE Sifain, N Lubbers, BT Nebgen, JS Smith, AY Lokhov, O Isayev, ... The journal of physical chemistry letters 9 (16), 4495-4501, 2018 | 123 | 2018 |
| Transferable dynamic molecular charge assignment using deep neural networks B Nebgen, N Lubbers, JS Smith, AE Sifain, A Lokhov, O Isayev, ... Journal of chemical theory and computation 14 (9), 4687-4698, 2018 | 106 | 2018 |
| Model independent direct detection analyses AL Fitzpatrick, W Haxton, E Katz, N Lubbers, Y Xu arXiv preprint arXiv:1211.2818, 2012 | 91 | 2012 |
| Automated discovery of a robust interatomic potential for aluminum JS Smith, B Nebgen, N Mathew, J Chen, N Lubbers, L Burakovsky, ... Nature communications 12 (1), 1257, 2021 | 81 | 2021 |
| Computationally efficient multiscale neural networks applied to fluid flow in complex 3D porous media JE Santos, Y Yin, H Jo, W Pan, Q Kang, HS Viswanathan, M Prodanović, ... Transport in porous media 140 (1), 241-272, 2021 | 80 | 2021 |
| 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 | 66 | 2022 |
| 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 | 60 | 2021 |
| Earthquake catalog‐based machine learning identification of laboratory fault states and the effects of magnitude of completeness N Lubbers, DC Bolton, J Mohd‐Yusof, C Marone, K Barros, PA Johnson Geophysical Research Letters 45 (24), 13,269-13,276, 2018 | 51 | 2018 |
| Machine learned Hückel theory: Interfacing physics and deep neural networks T Zubatiuk, B Nebgen, N Lubbers, JS Smith, R Zubatyuk, G Zhou, C Koh, ... The Journal of Chemical Physics 154 (24), 2021 | 40 | 2021 |
| Modeling nanoconfinement effects using active learning JE Santos, M Mehana, H Wu, M Prodanovic, Q Kang, N Lubbers, ... The Journal of Physical Chemistry C 124 (40), 22200-22211, 2020 | 40 | 2020 |
| 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 | 38 | 2023 |
| A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO2 sequestration accelerated with machine learning H Wu, N Lubbers, HS Viswanathan, RM Pollyea Applied Energy 287, 116580, 2021 | 34 | 2021 |
Kipton BarrosLos Alamos National Laboratory在 lanl.gov 的电子邮件经过验证
