Kipton Barros
Cited by
Cited by
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
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
Machine learning predicts laboratory earthquakes
B Rouet‐Leduc, C Hulbert, N Lubbers, K Barros, CJ Humphreys, ...
Geophysical Research Letters 44 (18), 9276-9282, 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
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
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
Inferring low-dimensional microstructure representations using convolutional neural networks
N Lubbers, T Lookman, K Barros
Physical Review E 96 (5), 052111, 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
Dielectric effects in the self-assembly of binary colloidal aggregates
K Barros, E Luijten
Physical review letters 113 (1), 017801, 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
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
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
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
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
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
Efficient and accurate simulation of dynamic dielectric objects
K Barros, D Sinkovits, E Luijten
The Journal of chemical physics 140 (6), 2014
Exotic magnetic orderings in the kagome Kondo-lattice model
K Barros, JWF Venderbos, GW Chern, CD Batista
Physical Review B 90 (24), 245119, 2014
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
Learning molecular energies using localized graph kernels
G Ferré, T Haut, K Barros
The Journal of chemical physics 146 (11), 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
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Articles 1–20