Standout Papers

Machine learning unifies the modeling of materials and molecules 2015 2026 2018 2022 477
  1. Machine learning unifies the modeling of materials and molecules (2017)
    Albert P. Bartók, Sandip De et al. Science Advances
  2. Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces (2015)
    Zhenwei Li, James R. Kermode et al. Physical Review Letters
  3. Machine Learning a General-Purpose Interatomic Potential for Silicon (2018)
    Albert P. Bartók, James R. Kermode et al. Physical Review X
  4. Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum (2018)
    Olga Barrera, David Bombač et al. Journal of Materials Science

Immediate Impact

1 by Nobel laureates 23 from Science/Nature 140 standout
Sub-graph 1 of 15

Citing Papers

Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions
2024 Standout
Ligand-channel-enabled ultrafast Li-ion conduction
2024 StandoutNature
175 intermediate papers

Works of James R. Kermode being referenced

Machine Learning a General-Purpose Interatomic Potential for Silicon
2018 Standout
Imeall: A computational framework for the calculation of the atomistic properties of grain boundaries
2018
and 17 more

Author Peers

Author Last Decade Papers Cites
James R. Kermode 2179 282 452 455 50 2.7k
Kamal Choudhary 2810 416 348 445 98 3.6k
Garritt J. Tucker 2699 271 261 148 71 3.1k
Daniel W. Davies 2441 371 414 662 48 3.6k
Shyam Dwaraknath 2597 298 147 219 44 3.4k
Nino Boccara 1284 338 454 159 35 2.9k
Jonathan Schmidt 1880 259 456 311 33 2.9k
F. Lärché 1813 306 409 172 41 3.1k
Jason Hattrick‐Simpers 1856 304 237 210 97 2.7k
Francesca Tavazza 2481 391 710 294 80 3.4k
Matous Mrovec 2140 195 366 82 80 2.7k

All Works

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2026