Justin S. Smith

发表

Closeout Report: Machine Learned Effective Hamiltonians for Molecular Properties

Sergei Tretiak, Benjamin Nebgen, Nicholas Lubbers, 2021 .

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Olexandr Isayev, Sergei Tretiak, Benjamin Nebgen, 2021 .

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Kipton Barros, Olexandr Isayev, Sergei Tretiak, 2019, Nature Communications.

Hierarchical modeling of molecular energies using a deep neural network.

Justin S. Smith, N. Lubbers, K. Barros, 2017, The Journal of chemical physics.

Discovering a Transferable Charge Assignment Model Using Machine Learning.

Kipton Barros, Olexandr Isayev, Sergei Tretiak, 2018, The journal of physical chemistry letters.

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

Olexandr Isayev, Adrian E Roitberg, Justin S. Smith, 2017, Scientific Data.

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Justin S. Smith, O. Isayev, A. Roitberg, 2020, Scientific Data.

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.

Justin S. Smith, O. Isayev, A. Roitberg, 2018, Journal of chemical theory and computation.

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

Justin S. Smith, O. Isayev, A. Roitberg, 2020, J. Chem. Inf. Model..

Machine learned Hückel theory: Interfacing physics and deep neural networks.

Justin S. Smith, O. Isayev, N. Lubbers, 2021, The Journal of chemical physics.

Quantum-Based Molecular Dynamics Simulations Using Tensor Cores.

Emanuel H. Rubensson, Justin S. Smith, A. Niklasson, 2021, Journal of chemical theory and computation.

Machine learning for molecular dynamics with strongly correlated electrons

Justin S. Smith, N. Lubbers, K. Barros, 2018, Physical Review B.

Teaching a neural network to attach and detach electrons from molecules

Justin S. Smith, O. Isayev, B. Nebgen, 2020, Nature Communications.

Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens.

Justin S. Smith, O. Isayev, A. Roitberg, 2020, Journal of chemical theory and computation.

Extending machine learning beyond interatomic potentials for predicting molecular properties

Richard A. Messerly, Justin S. Smith, O. Isayev, 2022, Nature Reviews Chemistry.

Extending machine learning beyond interatomic potentials for predicting molecular properties

Richard A. Messerly, Justin S. Smith, O. Isayev, 2022, Nature Reviews Chemistry.

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Justin S. Smith, O. Isayev, A. Roitberg, 2019, Scientific Data.

Extending machine learning beyond interatomic potentials for predicting molecular properties

Richard A. Messerly, Justin S. Smith, O. Isayev, 2022, Nature Reviews Chemistry.

Enantioselectivity in the Noyori–Ikariya Asymmetric Transfer Hydrogenation of Ketones

Pavel A. Dub, Justin S. Smith, S. Tretiak, 2021, Organometallics.

The Rise of Neural Networks for Materials and Chemical Dynamics.

Ying Wai Li, Justin S. Smith, N. Lubbers, 2021, The journal of physical chemistry letters.

Less is more: sampling chemical space with active learning

Adrian E. Roitberg, Nicholas Lubbers, Olexandr Isayev, 2018, The Journal of chemical physics.

Transforming Computational Drug Discovery with Machine Learning and AI.

Olexandr Isayev, Adrian E Roitberg, Justin S Smith, 2018, ACS medicinal chemistry letters.

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Justin S. Smith, O. Isayev, A. Roitberg, 2019, Nature Communications.

pH Effects and Cooperativity among Key Titratable Residues for Escherichia coli Glycinamide Ribonucleotide Transformylase.

Justin S. Smith, A. Roitberg, Pancham Lal Gupta, 2021, The journal of physical chemistry. B.

Uncertainty-driven dynamics for active learning of interatomic potentials

Richard A. Messerly, Justin S. Smith, N. Lubbers, 2023, Nature Computational Science.

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

Jerzy Leszczynski, Roman Zubatyuk, Justin S Smith, 2018, Science Advances.

Automated discovery of a robust interatomic potential for aluminum pdf

Nicholas Lubbers, Kipton Barros, Hai Ah Nam, 2020, Nature Communications.

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Justin S. Smith, O. Isayev, A. Roitberg, 2020, Scientific Data.

Predicting phosphorescence energies and inferring wavefunction localization with machine learning

Kipton Barros, Sergei Tretiak, Andrew Sifain, 2021, Chemical science.

Lightweight and effective tensor sensitivity for atomistic neural networks.

Justin S. Smith, N. Lubbers, K. Barros, 2022, The Journal of chemical physics.

Quantification of δ-Opioid Receptors in Human Brain with N1′ -([11C]Methyl) Naltrindole and Positron Emission Tomography

Justin S. Smith, J. Price, J. Zubieta, 1999 .

Machine learning potentials with Iterative Boltzmann Inversion: training to experiment

Richard A. Messerly, Justin S. Smith, N. Lubbers, 2023, 2307.04712.

ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules pdf

Adrian E. Roitberg, Olexandr Isayev, Justin S. Smith, 2017, ArXiv.

Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison.

Justin S. Smith, A. Garcia, D. Rosenberger, 2021, The journal of physical chemistry. B.

Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective.

Emanuel H. Rubensson, Justin S. Smith, A. Niklasson, 2021, Journal of chemical theory and computation.

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

Richard A. Messerly, Justin S. Smith, N. Lubbers, 2023, npj Computational Materials.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

J S Smith, O Isayev, A E Roitberg, 2016, Chemical science.

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

Justin S. Smith, O. Isayev, R. Zubatyuk, 2018, Science Advances.

ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules

Justin S. Smith, O. Isayev, A. Roitberg, 2017, ArXiv.

Automated discovery of a robust interatomic potential for aluminum

Justin S. Smith, T. Germann, N. Lubbers, 2020, Nature Communications.

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Richard A. Messerly, Justin S. Smith, O. Isayev, 2024, Nature chemistry.