Standout Papers

Analytical potential for atomistic simulations of silicon, carbon, and silicon carbide 2005 2026 2012 2019 522
  1. Analytical potential for atomistic simulations of silicon, carbon, and silicon carbide (2005)
    Paul Erhart, Karsten Albe Physical Review B
  2. The Hiphive Package for the Extraction of High‐Order Force Constants by Machine Learning (2019)
    Fredrik Eriksson, Erik Fransson et al. Advanced Theory and Simulations
  3. GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations (2022)
    Zheyong Fan, Yanzhou Wang et al. The Journal of Chemical Physics

Immediate Impact

2 by Nobel laureates 15 from Science/Nature 57 standout
Sub-graph 1 of 21

Citing Papers

Controlling mass and energy diffusion with metamaterials
2024 Standout
Monitoring Ti3C2Tx MXene Degradation Pathways Using Raman Spectroscopy
2024 Standout

Works of Paul Erhart being referenced

Surface Functionalization of 2D MXenes: Trends in Distribution, Composition, and Electronic Properties
2021
Extremely anisotropic van der Waals thermal conductors
2021 Nature

Author Peers

Author Last Decade Papers Cites
Paul Erhart 7482 2951 1224 1567 187 9.5k
A. S. Nowick 8383 2379 1529 1997 156 11.6k
Florian Banhart 12143 4142 1698 1616 177 15.1k
Vidvuds Ozoliņš 8930 4197 1939 2534 145 12.7k
Christian Elsässer 4666 2041 1248 1410 170 6.3k
Igor Levin 7131 3410 543 3080 181 9.1k
M.M.J. Treacy 9696 2202 2296 1123 173 13.5k
Tilmann Hickel 6236 1909 1343 1620 158 8.8k
Richard G. Hennig 10087 4472 1800 1749 193 12.7k
Kevin F. McCarty 7399 2603 1912 961 162 9.3k
Cai‐Zhuang Wang 6512 2622 2789 1158 348 10.0k

All Works

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Rankless by CCL
2026