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Nicholas LubbersComputer, Computational, and Statistical Sciences Division, Los Alamos National LaboratoryEmail yang diverifikasi di lanl.gov
Justin S SmithNVIDIAEmail yang diverifikasi di nvidia.com
Sergei TretiakLos Alamos National LaboratoryEmail yang diverifikasi di lanl.gov
Benjamin NebgenEmail yang diverifikasi di cornell.edu
Cristian BatistaUniversity of TennesseeEmail yang diverifikasi di utk.edu
Olexandr IsayevCarl and Amy Jones Professor of Chemistry, Carnegie Mellon UniversityEmail yang diverifikasi di olexandrisayev.com
Adrian RoitbergFrank Harris Professor. Department of Chemistry. University of Florida.Email yang diverifikasi di ufl.edu
Roman ZubatyukNVIDIAEmail yang diverifikasi di nvidia.com
Ying Wai LiLos Alamos National LaboratoryEmail yang diverifikasi di lanl.gov
Richard A. MesserlyOak Ridge National LaboratoryEmail yang diverifikasi di ornl.gov
Bertrand Rouet-LeducAssistant Professor, Kyoto University - Co-founder, GeolabeEmail yang diverifikasi di kyoto-u.ac.jp
David A. DahlbomPostdoc, Neutron Scattering Division, Oak Ridge National LaboratoryEmail yang diverifikasi di ornl.gov
Hao ZhangLos Alamos National LaboratoryEmail yang diverifikasi di lanl.gov
Turab LookmanLos Alamos National LaboratoryEmail yang diverifikasi di lanl.gov
Maksim KulichenkoLos Alamos National LaboratoryEmail yang diverifikasi di lanl.gov
Benjamin Cohen-SteadUniversity of Tennessee, KnoxvilleEmail yang diverifikasi di utk.edu
Zhentao WangZhejiang UniversityEmail yang diverifikasi di zju.edu.cn
Richard ScalettarProfessor of Physics, University of California, DavisEmail yang diverifikasi di physics.ucdavis.edu
Colin HumphreysQueen Mary University of LondonEmail yang diverifikasi di qmul.ac.uk
Nikita FedikStaff Scientist, Los Alamos National LaboratoryEmail yang diverifikasi di lanl.gov

Ikuti

Kipton Barros

Los Alamos National Laboratory

Email yang diverifikasi di lanl.gov - Beranda

Statistical physics Computational physics Machine learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	707	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	653	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	451	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	330	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	242	2020
Inferring low-dimensional microstructure representations using convolutional neural networks N Lubbers, T Lookman, K Barros Physical Review E 96 (5), 052111, 2017	153	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	124	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	123	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	109	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	101	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	99	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	96	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	96	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	80	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

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