Standout Papers

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science 2019 2026 2021 2023 612
  1. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science (2019)
    Volker L. Deringer, A. Miguel et al. Advanced Materials

Immediate Impact

12 from Science/Nature 73 standout
Sub-graph 1 of 17

Citing Papers

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20 intermediate papers

Works of A. Miguel being referenced

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
2019 Standout
Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
2019
and 6 more

Author Peers

Author Last Decade Papers Cites
A. Miguel 2015 645 665 915 70 3.1k
B. Hourahine 1588 419 809 927 88 2.8k
Rickard Armiento 2188 284 849 832 70 3.1k
Luca M. Ghiringhelli 3040 205 550 1285 72 3.9k
V.V. Zhirnov 1983 128 689 1635 92 3.4k
Roman Engel‐Herbert 3070 490 853 2352 111 5.1k
Matthew K. Horton 1376 320 206 580 51 2.0k
D. O. Demchenko 3337 1158 554 1792 60 4.3k
Hiori Kino 1794 857 1143 1123 90 3.6k
S. A. Brown 1027 408 742 1027 133 2.5k
Erik Bitzek 2669 185 585 376 76 3.6k

All Works

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2026