Kipton Barros

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Nicholas LubbersComputer, Computational, and Statistical Sciences Division, Los Alamos National LaboratoryDirección de correo verificada de lanl.gov
Justin S SmithNVIDIADirección de correo verificada de nvidia.com
Sergei TretiakLos Alamos National LaboratoryDirección de correo verificada de lanl.gov
Benjamin NebgenDirección de correo verificada de cornell.edu
Cristian BatistaUniversity of TennesseeDirección de correo verificada de utk.edu
Olexandr IsayevCarl and Amy Jones Professor of Chemistry, Carnegie Mellon UniversityDirección de correo verificada de olexandrisayev.com
Adrian RoitbergFrank Harris Professor. Department of Chemistry. University of Florida.Dirección de correo verificada de ufl.edu
Roman ZubatyukNVIDIADirección de correo verificada de nvidia.com
Ying Wai LiLos Alamos National LaboratoryDirección de correo verificada de lanl.gov
Richard A. MesserlyOak Ridge National LaboratoryDirección de correo verificada de ornl.gov
Bertrand Rouet-LeducAssistant Professor, Kyoto University - Co-founder, GeolabeDirección de correo verificada de kyoto-u.ac.jp
David A. DahlbomPostdoc, Neutron Scattering Division, Oak Ridge National LaboratoryDirección de correo verificada de ornl.gov
Hao ZhangLos Alamos National LaboratoryDirección de correo verificada de lanl.gov
Turab LookmanLos Alamos National LaboratoryDirección de correo verificada de lanl.gov
Maksim KulichenkoLos Alamos National LaboratoryDirección de correo verificada de lanl.gov
Benjamin Cohen-SteadUniversity of Tennessee, KnoxvilleDirección de correo verificada de utk.edu
Richard ScalettarProfessor of Physics, University of California, DavisDirección de correo verificada de physics.ucdavis.edu
Colin HumphreysQueen Mary University of LondonDirección de correo verificada de qmul.ac.uk
Timothy C. GermannLos Alamos National LaboratoryDirección de correo verificada de lanl.gov
Nikita FedikStaff Scientist, Los Alamos National LaboratoryDirección de correo verificada de lanl.gov

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Kipton Barros

Los Alamos National Laboratory

Dirección de correo verificada de lanl.gov - Página principal

Statistical physics Computational physics Machine learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	700	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	651	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	342	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	323	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	149	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	136	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	119	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	102	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	97	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	93	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	75	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

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