Justin S. Smith

Active 1993–2026

Also published as
Justin S Smith
324
Papers
38,448
Citations
110
h-index
292
i10-index

Citations

Citations per year for Justin S. Smith1992: 1 citations1994: 1 citations1995: 3 citations1996: 11 citations1997: 13 citations1998: 12 citations1999: 13 citations2000: 30 citations2001: 59 citations2002: 52 citations2003: 61 citations2004: 62 citations2005: 50 citations2006: 56 citations2007: 32 citations2008: 44 citations2009: 62 citations2010: 88 citations2011: 103 citations2012: 149 citations2013: 157 citations2014: 222 citations2015: 339 citations2016: 226 citations2017: 311 citations2018: 267 citations2019: 1,385 citations2020: 1,441 citations2021: 1,421 citations2022: 1,141 citations2023: 872 citations2024: 1,393 citations2025: 833 citations2026: 72 citations1993: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 2,798 citing papers, 33.3% of this breakdownChina: 735 citing papers, 8.7% of this breakdownUnited Kingdom: 453 citing papers, 5.4% of this breakdownGermany: 448 citing papers, 5.3% of this breakdownCanada: 433 citing papers, 5.2% of this breakdownJapan: 357 citing papers, 4.2% of this breakdownFrance: 329 citing papers, 3.9% of this breakdownSwitzerland: 308 citing papers, 3.7% of this breakdownItaly: 260 citing papers, 3.1% of this breakdownSouth Korea: 253 citing papers, 3% of this breakdownNetherlands: 165 citing papers, 2% of this breakdownSpain: 162 citing papers, 1.9% of this breakdown
0%33.3%Other 20.3%

Fields

  • Medicine60.8%
  • Materials Science18.4%
  • Biochemistry, Genetics and Molecular Biology7.1%
  • Computer Science6.8%
  • Engineering2.7%
  • Neuroscience1.4%
  • Other2.8%

Topics

  • Spine and Intervertebral Disc Pathology9.3%
  • Scoliosis diagnosis and treatment9%
  • Machine Learning in Materials Science7.7%
  • Computational Drug Discovery Methods6.3%
  • Cervical and Thoracic Myelopathy5.7%
  • Spinal Fractures and Fixation Techniques5%
  • Other57%

Coauthors

All papers

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

    Authors: , , - Chemical Science 2017 cited by 2,028

  2. Less is more: sampling chemical space with active learning

    Authors: , , , , - The Journal of Chemical Physics 2018 cited by 846

  3. Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

    Authors: , , , , , , - Journal of Chemical Theory and Computation 2020 cited by 403

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

    Authors: , , , , , , , , - Nature Communications 2018 cited by 655

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

    Authors: , , , , - Journal of Chemical Information and Modeling, J. Chem. Inf. Model. 2020 cited by 324

  6. The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

    Authors: , , , , , , , - Scientific Data 2020 cited by 275

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

    Authors: , , , - Science Advances 2019 cited by 300

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

    Authors: , , - Scientific Data 2017 cited by 333

  9. The Aging of the Global Population

    Authors: , , , , , , , - Neurosurgery 2015 cited by 377

  10. Hierarchical modeling of molecular energies using a deep neural network

    Authors: , , - The Journal of Chemical Physics 2018 cited by 297

  11. Radiographical Spinopelvic Parameters and Disability in the Setting of Adult Spinal Deformity

    Authors: , , , , , , , , , , , , , , - Spine 2013 cited by 1,055

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

    Authors: , , , , , , , , , , - Nature Chemistry 2024 cited by 121

  13. Extending machine learning beyond interatomic potentials for predicting molecular properties

    Authors: , , , , , , , , , , , - Nature Reviews Chemistry 2022 cited by 154

  14. Defining Spino-Pelvic Alignment Thresholds

    Authors: , , , , , , , , , , , , , , - Spine 2015 cited by 469

  15. Teaching a neural network to attach and detach electrons from molecules

    Authors: , , , , - Nature Communications 2021 cited by 123

  16. Role of Extent of Resection in the Long-Term Outcome of Low-Grade Hemispheric Gliomas

    Authors: , , , , , , , , , - Journal of Clinical Oncology 2008 cited by 1,276

  17. The Health Impact of Symptomatic Adult Spinal Deformity

    Authors: , , , , , , , , , , , , , , , , , , - Spine 2015 cited by 306

  18. Efficacy and Safety of Surgical Decompression in Patients with Cervical Spondylotic Myelopathy

    Authors: , , , , , , , , , , , , , , , , , - Journal of Bone and Joint Surgery 2013 cited by 501

  19. The Rise of Neural Networks for Materials and Chemical Dynamics

    Authors: , , , , , , , , - The Journal of Physical Chemistry Letters 2021 cited by 100

  20. The SRS-Schwab Adult Spinal Deformity Classification

    Authors: , , , , , , , , , , , , , , , - Neurosurgery 2013 cited by 428

  21. Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery

    Authors: , , , , , , , , , , , , , , , , - Spine 2019 cited by 142

  22. Data Generation for Machine Learning Interatomic Potentials and Beyond

    Authors: , , , , , , , , , , , - Chemical Reviews 2024 cited by 132

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

    Authors: , , , , , , - npj Computational Materials 2024 cited by 42

  24. Adult Spinal Deformity: Epidemiology, Health Impact, Evaluation, and Management

    Authors: , , , , , , , , , , , - Spine Deformity 2016 cited by 280