Kipton Barros

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Citations	3304	2637
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i10-index	44	39

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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 IsayevAssistant 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
Bertrand Rouet-LeducAssistant Professor, Kyoto UniversityVerified email at kyoto-u.ac.jp
Turab LookmanLos Alamos National LaboratoryVerified email at lanl.gov
Ying Wai LiLos Alamos National LaboratoryVerified email at lanl.gov
Colin HumphreysQueen Mary University of LondonVerified email at qmul.ac.uk
Richard C. BrowerBoston University, Department of Electrical and Computer Engineering and PhysicsVerified email at bu.edu
Andrew E. SifainRoivant SciencesVerified email at usc.edu
Zhentao WangZhejiang UniversityVerified email at zju.edu.cn
Timothy C. GermannLos Alamos National LaboratoryVerified email at lanl.gov
Andrey LokhovLos Alamos National Laboratory, Theoretical DivisionVerified email at lanl.gov
Paul A. JohnsonLos Alamos National LaboratoryVerified email at lanl.gov
Claudia HulbertEcole Normale SuperieureVerified email at geologie.ens.fr
Benjamin Cohen-SteadUniversity of Tennessee Knoxville Department of Physics and AstronomyVerified email at utk.edu

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	530	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	403	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	301	2017
Hierarchical modeling of molecular energies using a deep neural network N Lubbers, JS Smith, K Barros The Journal of chemical physics 148 (24), 241715, 2018	232	2018
Inferring low-dimensional microstructure representations using convolutional neural networks N Lubbers, T Lookman, K Barros Physical Review E 96 (5), 052111, 2017	113	2017
Dielectric effects in the self-assembly of binary colloidal aggregates K Barros, E Luijten Physical review letters 113 (1), 017801, 2014	113	2014
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	112	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	98	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	97	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	94	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	85	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	84	2009
Efficient and accurate simulation of dynamic dielectric objects K Barros, D Sinkovits, E Luijten The Journal of chemical physics 140 (6), 064903, 2014	69	2014
Efficient Langevin simulation of coupled classical fields and fermions K Barros, Y Kato Physical Review B 88 (23), 235101, 2013	68	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	59	2020
Learning molecular energies using localized graph kernels G Ferré, T Haut, K Barros The Journal of chemical physics 146 (11), 114107, 2017	59	2017
Exotic magnetic orderings in the kagome Kondo-lattice model K Barros, JWF Venderbos, GW Chern, CD Batista Physical Review B 90 (24), 245119, 2014	57	2014
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), 1-6, 2016	43	2016
Direct observation of stringlike collective motion in a two-dimensional driven granular fluid CR Berardi, K Barros, JF Douglas, W Losert Physical Review E 81 (4), 041301, 2010	40	2010
Resistivity minimum in highly frustrated itinerant magnets Z Wang, K Barros, GW Chern, DL Maslov, CD Batista Physical review letters 117 (20), 206601, 2016	39	2016

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