Nicholas Lubbers

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Kipton BarrosLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Benjamin NebgenAdresse e-mail validée de cornell.edu
Justin S SmithNVIDIAAdresse e-mail validée de nvidia.com
Sergei TretiakLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Olexandr IsayevCarl and Amy Jones Professor of Chemistry, Carnegie Mellon UniversityAdresse e-mail validée de olexandrisayev.com
Adrian RoitbergFrank Harris Professor. Department of Chemistry. University of Florida.Adresse e-mail validée de ufl.edu
Hari ViswanathanSenior Scientist, Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Roman ZubatyukCarnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
Javier E. SantosCenter for Nonlinear Studies, Los Alamos National LabAdresse e-mail validée de lanl.gov
Richard A. MesserlyOak Ridge National LaboratoryAdresse e-mail validée de ornl.gov
Timothy C. GermannLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Qinjun KangScientist, Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Ying Wai LiLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Daniel LivescuScientist, Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Maksim KulichenkoLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Paul A. JohnsonLos Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Mohamed MehanaLos Alamos National LabAdresse e-mail validée de lanl.gov
A. Liam FitzpatrickAssociate Professor, Boston UniversityAdresse e-mail validée de bu.edu
Arvind T. MohanScientist, Computational Physics and Methods Group, Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov
Nikita FedikStaff Scientist, Los Alamos National LaboratoryAdresse e-mail validée de lanl.gov

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Nicholas Lubbers

Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory

Adresse e-mail validée de lanl.gov


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Less is more: Sampling chemical space with active learning JS Smith, B Nebgen, N Lubbers, O Isayev, AE Roitberg The Journal of chemical physics 148 (24), 2018	744	2018
The effective field theory of dark matter direct detection AL Fitzpatrick, W Haxton, E Katz, N Lubbers, Y Xu Journal of Cosmology and Astroparticle Physics 2013 (02), 004, 2013	713	2013
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	669	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	448	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	331	2018
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	226	2020
Inferring low-dimensional microstructure representations using convolutional neural networks N Lubbers, T Lookman, K Barros Physical Review E 96 (5), 052111, 2017	146	2017
Embedding hard physical constraints in neural network coarse-graining of 3D turbulence AT Mohan, N Lubbers, D Livescu, M Chertkov arXiv preprint arXiv:2002.00021, 2020	138*	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	129	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	113	2018
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	95	2021
Model independent direct detection analyses AL Fitzpatrick, W Haxton, E Katz, N Lubbers, Y Xu arXiv preprint arXiv:1211.2818, 2012	94	2012
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	89	2022
Computationally efficient multiscale neural networks applied to fluid flow in complex 3D porous media JE Santos, Y Yin, H Jo, W Pan, Q Kang, HS Viswanathan, M Prodanović, ... Transport in porous media 140 (1), 241-272, 2021	87	2021
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	68	2021
Uncertainty-driven dynamics for active learning of interatomic potentials M Kulichenko, K Barros, N Lubbers, YW Li, R Messerly, S Tretiak, ... Nature Computational Science 3 (3), 230-239, 2023	57	2023
Earthquake catalog‐based machine learning identification of laboratory fault states and the effects of magnitude of completeness N Lubbers, DC Bolton, J Mohd‐Yusof, C Marone, K Barros, PA Johnson Geophysical Research Letters 45 (24), 13,269-13,276, 2018	57	2018
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential S Zhang, MZ Makoś, RB Jadrich, E Kraka, K Barros, BT Nebgen, S Tretiak, ... Nature Chemistry 16 (5), 727-734, 2024	48	2024
Modeling nanoconfinement effects using active learning JE Santos, M Mehana, H Wu, M Prodanovic, Q Kang, N Lubbers, ... The Journal of Physical Chemistry C 124 (40), 22200-22211, 2020	43	2020
Machine learned Hückel theory: Interfacing physics and deep neural networks T Zubatiuk, B Nebgen, N Lubbers, JS Smith, R Zubatyuk, G Zhou, C Koh, ... The Journal of Chemical Physics 154 (24), 2021	42	2021

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