Nicholas Lubbers
Nicholas Lubbers
Staff Scientist, Computer, Computational, and Statistical Sciences Division, LANL
Potvrđena adresa e-pošte na
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
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
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
Machine learning predicts laboratory earthquakes
B Rouet‐Leduc, C Hulbert, N Lubbers, K Barros, CJ Humphreys, ...
Geophysical Research Letters 44 (18), 9276-9282, 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
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
Inferring low-dimensional microstructure representations using convolutional neural networks
N Lubbers, T Lookman, K Barros
Physical Review E 96 (5), 052111, 2017
Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence
AT Mohan, N Lubbers, M Chertkov, D Livescu
Physical Review Fluids 8 (1), 014604, 2023
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
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
Model independent direct detection analyses
AL Fitzpatrick, W Haxton, E Katz, N Lubbers, Y Xu
arXiv preprint arXiv:1211.2818, 2012
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
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
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
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
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
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
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
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
Machine learning for molecular dynamics with strongly correlated electrons
H Suwa, JS Smith, N Lubbers, CD Batista, GW Chern, K Barros
Physical Review B 99 (16), 161107, 2019
Sustav trenutno ne može provesti ovu radnju. Pokušajte ponovo kasnije.
Članci 1–20