Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

Article Properties

Language

English
DOI (url)

10.1103/physrevlett.98.146401
Publication Date

2007/04/02
Journal

Physical Review Letters
Indian UGC (Journal)
Refrences

17
Citations

382
Jörg Behler
Michele Parrinello

Cite

Behler, Jörg, and Michele Parrinello. “Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces”. Physical Review Letters, vol. 98, no. 14, 2007, https://doi.org/10.1103/physrevlett.98.146401.

Behler, J., & Parrinello, M. (2007). Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Physical Review Letters, 98(14). https://doi.org/10.1103/physrevlett.98.146401

Behler J, Parrinello M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Physical Review Letters. 2007;98(14).

Journal Categories

Science

Chemistry

Physical and theoretical chemistry

Science

Physics

Refrences

Citations

Citations Analysis

Category	Category Repetition
Science: Chemistry	353
Science: Chemistry: Physical and theoretical chemistry	324
Science: Physics: Atomic physics. Constitution and properties of matter	308
Technology: Chemical technology	33
Science: Physics	31

The first research to cite this article was titled A neural-network potential through charge equilibration for WS2: From clusters to sheets and was published in 2017. The most recent citation comes from a 2024 study titled A neural-network potential through charge equilibration for WS2: From clusters to sheets . This article reached its peak citation in 2020 , with 77 citations.It has been cited in 39 different journals, 33% of which are open access. Among related journals, the The Journal of Chemical Physics cited this research the most, with 287 citations. The chart below illustrates the annual citation trends for this article.