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Reference changes · DOI
Behler, Perspective: Machine learning potentials for atom- istic simulations, Journal of Chemical Physics 145 (2016) 170901.doi:10.1063/1.4966192
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Crossref
4 open · 4 total · 0 disputed
- Event date
- 2016-12-03
01One-hop citing occurrences
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Open
Learning and Interpreting Potentials for Classical Hamiltonian Systems
ref [2] ·
1907.11806
· notice #2351
· dispute
Raw extraction · bibliography line
Behler, J.: Perspective: Machine learning potentials for atomistic sim- ulations. The Journal of Chemical Physics 145(17), 170901 (2016). https://doi.org/10.1063/1.4966192
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Open
A density-functional perspective on force fields
ref [2] ·
2604.25215
· notice #2350
· dispute
Raw extraction · bibliography line
author author J. Behler ,\ 10.1063/1.4966192 journal journal The Journal of Chemical Physics \ volume 145 ,\ pages 170901 ( year 2016 ) NoStop
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author author J. Behler, 10.1063/1.4966192 journal journal The Journal of Chemical Physics volume 145, pages 170901 ( year 2016 )
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Open
Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics
ref [98] ·
2606.01763
· notice #2352
· dispute
Raw extraction · bibliography line
Behler, J. Perspective:. J. Chem. Phys. , volume =. doi:10.1063/1.4966192 , abstract =
Parser render (TeX stripped for reading; raw above is the evidence)
Behler, J. Perspective:. J. Chem. Phys., volume =. doi:10.1063/1.4966192, abstract =
Correction
Open
Adaptive fine-tuning of foundation models for crystal structure prediction: Discovery of high-pressure phases in the CaFeNi system
ref [9] ·
2606.30870
· notice #2353
· dispute
Raw extraction · bibliography line
J. Behler, Perspective: Machine learning potentials for atom- istic simulations, Journal of Chemical Physics 145 (2016) 170901.doi:10.1063/1.4966192