Kipton Barros

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	All	Since 2018
Citations	4111	3444
h-index	28	27
i10-index	57	52

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Nicholas LubbersStaff Scientist, Computer, Computational, and Statistical Sciences Division, LANLVerified email at bu.edu
Justin S SmithNVIDIAVerified email at nvidia.com
Sergei TretiakLos Alamos National LaboratoryVerified email at lanl.gov
Benjamin NebgenVerified email at cornell.edu
Cristian BatistaUniversity of TennesseeVerified email at utk.edu
Olexandr IsayevAssociate Professor, Carnegie Mellon UniversityVerified email at olexandrisayev.com
Adrian RoitbergUniversity of Florida, Chemistry DepartmentVerified email at ufl.edu
Roman ZubatyukCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Ying Wai LiLos Alamos National LaboratoryVerified email at lanl.gov
Bertrand Rouet-LeducAssistant Professor, Kyoto University - Co-founder, GeolabeVerified email at kyoto-u.ac.jp
Turab LookmanLos Alamos National LaboratoryVerified email at lanl.gov
Benjamin Cohen-SteadUniversity of Tennessee Knoxville Department of Physics and AstronomyVerified email at utk.edu
Colin HumphreysQueen Mary University of LondonVerified email at qmul.ac.uk
Richard ScalettarProfessor of Physics, University of California, DavisVerified email at physics.ucdavis.edu
Andrew E. SifainRoivant SciencesVerified email at usc.edu
Richard C. BrowerBoston University, Department of Electrical and Computer Engineering and PhysicsVerified email at bu.edu
Buck BabichRobotics R&D Manager, NVIDIA CorporationVerified email at nvidia.com
Richard A. MesserlyLos Alamos National LaboratoryVerified email at lanl.gov
Zhentao WangZhejiang UniversityVerified email at zju.edu.cn
Timothy C. GermannLos Alamos National LaboratoryVerified email at lanl.gov

Kipton Barros

Staff scientist, Theoretical Division, Los Alamos National Laboratory

Verified email at lanl.gov - Homepage

Statistical physics Computational physics Machine learning


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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	573	2010
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	520	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	363	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	287	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	195	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	147	2020
Inferring low-dimensional microstructure representations using convolutional neural networks N Lubbers, T Lookman, K Barros Physical Review E 96 (5), 052111, 2017	128	2017
Dielectric effects in the self-assembly of binary colloidal aggregates K Barros, E Luijten Physical review letters 113 (1), 017801, 2014	117	2014
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	114	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	104	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	100	2016
Freezing into stripe states in two-dimensional ferromagnets and crossing probabilities in critical percolation K Barros, PL Krapivsky, S Redner Physical Review E 80 (4), 040101, 2009	87	2009
Efficient Langevin simulation of coupled classical fields and fermions K Barros, Y Kato Physical Review B 88 (23), 235101, 2013	75	2013
Efficient and accurate simulation of dynamic dielectric objects K Barros, D Sinkovits, E Luijten The Journal of chemical physics 140 (6), 2014	74	2014
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	70	2020
Exotic magnetic orderings in the kagome Kondo-lattice model K Barros, JWF Venderbos, GW Chern, CD Batista Physical Review B 90 (24), 245119, 2014	63	2014
Learning molecular energies using localized graph kernels G Ferré, T Haut, K Barros The Journal of chemical physics 146 (11), 2017	61	2017
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	55	2021
Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning B Rouet-Leduc, K Barros, T Lookman, CJ Humphreys Scientific reports 6 (1), 24862, 2016	51	2016
Resistivity minimum in highly frustrated itinerant magnets Z Wang, K Barros, GW Chern, DL Maslov, CD Batista Physical review letters 117 (20), 206601, 2016	45	2016

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