Justin S Smith
Cited by
Cited by
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
JS Smith, O Isayev, AE Roitberg
Chemical Science 8 (4), 3192-3203, 2017
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), 241733, 2018
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
J S Smith, BT Nebgen, R Zubatyuk, N Lubbers, C Devereux, K Barros, ...
Nature Communications 10 (2903), 2019
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
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
JS Smith, O Isayev, AE Roitberg
Scientific Data 4, 170193, 2017
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
TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials
X Gao, F Ramezanghorbani, O Isayev, JS Smith, AE Roitberg
Journal of chemical information and modeling 60 (7), 3408-3415, 2020
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
R Zubatyuk, JS Smith, J Leszczynski, O Isayev
Science Advances 5 (8), eaav6490, 2019
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
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, 2018
Transforming Computational Drug Discovery with Machine Learning and AI
JS Smith, AE Roitberg, O Isayev
ACS medicinal chemistry letters 9 (11), 1065-1069, 2018
Teaching a neural network to attach and detach electrons from molecules
R Zubatyuk, JS Smith, BT Nebgen, S Tretiak, O Isayev
Nature Communications 12 (1), 4870, 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
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 of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison
D Rosenberger, JS Smith, AE Garcia
The Journal of Physical Chemistry B 125 (14), 3598-3612, 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
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
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