| 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 | 703 | 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 | 656 | 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 | 458 | 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 | 344 | 2018 |
| 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 | 327 | 2020 |
| 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 | 238 | 2020 |
| Inferring low-dimensional microstructure representations using convolutional neural networks N Lubbers, T Lookman, K Barros Physical Review E 96 (5), 052111, 2017 | 151 | 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 | 138 | 2018 |
| Dielectric effects in the self-assembly of binary colloidal aggregates K Barros, E Luijten Physical review letters 113 (1), 017801, 2014 | 125 | 2014 |
| 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 | 121 | 2018 |
| 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 | 110 | 2016 |
| 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 |
| Efficient Langevin simulation of coupled classical fields and fermions K Barros, Y Kato Physical Review B—Condensed Matter and Materials Physics 88 (23), 235101, 2013 | 100 | 2013 |
| 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 | 98 | 2021 |
| 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 | 94 | 2020 |
| 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 | 91 | 2009 |
| Efficient and accurate simulation of dynamic dielectric objects K Barros, D Sinkovits, E Luijten The Journal of chemical physics 140 (6), 2014 | 79 | 2014 |
| 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 |
| Exotic magnetic orderings in the kagome Kondo-lattice model K Barros, JWF Venderbos, GW Chern, CD Batista Physical Review B 90 (24), 245119, 2014 | 74 | 2014 |
| Learning molecular energies using localized graph kernels G Ferré, T Haut, K Barros The Journal of chemical physics 146 (11), 2017 | 69 | 2017 |
Nicholas LubbersComputer, Computational, and Statistical Sciences Division, Los Alamos National LaboratoryVerified email at lanl.gov
