| 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 | 822 | 2019 |
| Solving Lattice QCD systems of equations using mixed precision solvers on GPUs MA Clark, R Babich, K Barros, RC Brower, C Rebbi Computer Physics Communications 181 (9), 1517-1528, 2010 | 724 | 2010 |
| Machine learning predicts laboratory earthquakes B Rouet‐Leduc, C Hulbert, N Lubbers, K Barros, CJ Humphreys, ... Geophysical Research Letters 44 (18), 9276-9282, 2017 | 540 | 2017 |
| Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens C Devereux, JS Smith, KK Huddleston, K Barros, R Zubatyuk, O Isayev, ... Journal of chemical theory and computation 16 (7), 4192-4202, 2020 | 470 | 2020 |
| Hierarchical modeling of molecular energies using a deep neural network N Lubbers, JS Smith, K Barros The Journal of chemical physics 148 (24), 2018 | 392 | 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 | 350 | 2020 |
| Inferring low-dimensional microstructure representations using convolutional neural networks N Lubbers, T Lookman, K Barros Physical Review E 96 (5), 052111, 2017 | 165 | 2017 |
| 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 | 159 | 2018 |
| 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 | 153 | 2022 |
| 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 | 145 | 2023 |
| 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 | 134 | 2018 |
| Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential S Zhang, MZ Makoś, RB Jadrich, E Kraka, K Barros, BT Nebgen, S Tretiak, ... Nature Chemistry 16 (5), 727-734, 2024 | 131 | 2024 |
| Dielectric effects in the self-assembly of binary colloidal aggregates K Barros, E Luijten Physical review letters 113 (1), 017801, 2014 | 127 | 2014 |
| Vortex crystals with chiral stripes in itinerant magnets R Ozawa, S Hayami, K Barros, GW Chern, Y Motome, CD Batista Journal of the Physical Society of Japan 85 (10), 103703, 2016 | 125 | 2016 |
| 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 | 121 | 2021 |
| Data generation for machine learning interatomic potentials and beyond M Kulichenko, B Nebgen, N Lubbers, JS Smith, K Barros, AEA Allen, ... Chemical Reviews 124 (24), 13681-13714, 2024 | 106 | 2024 |
| Efficient Langevin simulation of coupled classical fields and fermions K Barros, Y Kato arXiv preprint arXiv:1303.1101, 2013 | 104 | 2013 |
| 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 | 103 | 2020 |
| 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 | 98 | 2021 |
| Freezing into stripe states in two-dimensional ferromagnets and crossing probabilities in critical percolation K Barros, PL Krapivsky, S Redner Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 80 (4 …, 2009 | 97 | 2009 |
Nicholas LubbersComputer, Computational, and Statistical Sciences Division, Los Alamos National LaboratoryVerified email at lanl.gov
