Standout Papers

Machine learning for molecular and materials science 2017 2026 2020 2023 2.8k
  1. Machine learning for molecular and materials science (2018)
    Keith T. Butler, Daniel W. Davies et al. Nature
  2. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost (2017)
    Justin S. Smith, Olexandr Isayev et al. Chemical Science
  3. Less is more: Sampling chemical space with active learning (2018)
    Justin S. Smith, Benjamin Nebgen et al. The Journal of Chemical Physics
  4. QSAR without borders (2020)
    Eugene Muratov, Jürgen Bajorath et al. Chemical Society Reviews
  5. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (2019)
    Justin S. Smith, Benjamin Nebgen et al. Nature Communications
  6. Universal fragment descriptors for predicting properties of inorganic crystals (2017)
    Olexandr Isayev, Corey Oses et al. Nature Communications
  7. Best practices in machine learning for chemistry (2021)
    Nongnuch Artrith, Keith T. Butler et al. Nature Chemistry
  8. Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens (2020)
    Christian Devereux, Justin S. Smith et al. Journal of Chemical Theory and Computation
  9. Generative Models as an Emerging Paradigm in the Chemical Sciences (2023)
    Dylan M. Anstine, Olexandr Isayev Journal of the American Chemical Society
  10. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR (2023)
    Alexander Tropsha, Olexandr Isayev et al. Nature Reviews Drug Discovery
  11. Machine Learning Interatomic Potentials and Long-Range Physics (2023)
    Dylan M. Anstine, Olexandr Isayev The Journal of Physical Chemistry A
  12. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential (2024)
    Shuhao Zhang, Ryan B. Jadrich et al. Nature Chemistry
  13. AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs (2025)
    Dylan M. Anstine, R.I. Zubatyuk et al. Chemical Science

Immediate Impact

2 by Nobel laureates 12 from Science/Nature 115 standout
Sub-graph 1 of 17

Citing Papers

Machine Learning in Polymer Research
2025 Standout
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules
2025 Standout
42 intermediate papers

Works of Olexandr Isayev being referenced

Teaching a neural network to attach and detach electrons from molecules
2021
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
2017
and 14 more

Author Peers

Author Last Decade Papers Cites
Olexandr Isayev 7254 3978 2605 95 10.2k
O. Anatole von Lilienfeld 8041 4327 2207 115 10.6k
Michele Ceriotti 6294 2024 1717 165 10.4k
Gábor Cśanyi 14020 3529 2089 168 17.2k
Jörg Behler 12518 3375 2190 116 14.9k
Albert P. Bartók 6687 2035 1143 50 8.0k
Matthias Rupp 4882 3081 1517 48 6.0k
J. M. Martı́nez 2366 3696 1452 222 14.6k
Ernesto G. Birgin 2110 1525 1267 109 11.1k
Heather J. Kulik 4051 995 1140 213 7.0k
Neil Robertson 3909 4627 650 324 15.7k

All Works

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Rankless by CCL
2026