Standout Papers

SchNet – A deep learning architecture for molecules and materials 2017 2026 2020 2023 1.3k
  1. SchNet – A deep learning architecture for molecules and materials (2018)
    Kristof T. Schütt, Huziel E. Sauceda et al. The Journal of Chemical Physics
  2. Machine learning of accurate energy-conserving molecular force fields (2017)
    Stefan Chmiela, Alexandre Tkatchenko et al. Science Advances
  3. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects (2021)
    Oliver T. Unke, Stefan Chmiela et al. Nature Communications
  4. Accurate global machine learning force fields for molecules with hundreds of atoms (2023)
    Stefan Chmiela, Valentín Vassilev-Galindo et al. Science Advances

Immediate Impact

3 by Nobel laureates 24 from Science/Nature 126 standout
Sub-graph 1 of 18

Citing Papers

Targeting protein–ligand neosurfaces with a generalizable deep learning tool
2025 StandoutNature
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules
2025 Standout
118 intermediate papers

Works of Huziel E. Sauceda being referenced

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
2021 Standout
SchNet – A deep learning architecture for molecules and materials
2018 Standout
and 5 more

Author Peers

Author Last Decade Papers Cites
Huziel E. Sauceda 2433 793 445 1165 24 2.8k
Stefan Chmiela 2330 873 391 1266 21 2.9k
Raghunathan Ramakrishnan 2398 756 518 1573 33 2.9k
Benjamin Nebgen 1773 678 792 940 44 2.5k
Katja Hansen 1674 552 400 1175 15 2.2k
Kristof T. Schütt 3775 1208 590 1988 21 4.5k
Michael Gastegger 1604 566 354 836 24 2.0k
Philippe Schwaller 2727 800 249 1270 47 3.6k
Daniel W. Davies 2441 322 414 662 48 3.6k
Justin S. Smith 3338 1435 682 2048 59 4.6k
Pavlo O. Dral 2950 966 1095 1692 79 4.0k

All Works

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2026