High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

By Nongnuch Artrith, Tobias Morawietz, J. Behler. 2011

295 citations recorded by the source corpus.

Subfields: Materials Chemistry

How researchers see this paper

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In Materials Chemistry

1. Yuri Mishin
2. Sam Schoenholz
3. Rohit Batra
4. Gábor Csányi
5. Rampi Ramprasad
6. James R. Kermode
7. Noam Bernstein
8. Florian Hase

Citations in the corpus

• Deep Potentials for Materials Science (2022, background): “Although there are research reports which introduce long-range interaction in the model construction [26, 28, 47–55], there is still no widely accepted method which can handle this interaction appropriately.”
• End-to-End Differentiable , Hardware Accelerated , Molecular Dynamics in Pure Python (2019, methodology): “, 2019), as well as making MD simulations faster and more accurate (Behler & Parrinello, 2007; Behler, 2011; Artrith et al., 2011; Artrith & Behler, 2012; Artrith et al., 2018; Deringer et al., 2018a; Bartók et al., 2018; Yao et al., 2018; Seko et al., 2015; Deringer et al., 2018b; Cubuk et al., 201”
• Machine-learned potentials for next-generation matter simulations (2021, background): “5b) 22,44,45,68,105 , simulate the impact and propagation of lattice defects with atomistic resolution 75,106 , simulate nanoparticles (Fig.”