Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks
By S. Lorenz, A. Gross, Matthias Scheffler. 2004
336 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry, Physical and Theoretical Chemistry
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
- K. Müller
- Grgoire Montavon
- Alexandre Tkatchenko
- Matthias Rupp
- O. A. von Lilienfeld
- Oliver T. Unke
- Kristof T. Schütt
- Stéphane Mallat
In Materials Chemistry
- Grgoire Montavon
- K. Müller
- Alexandre Tkatchenko
- Matthias Rupp
- Katja Hansen
- A. Pais
- Tânia F Cova
- Wojciech Samek
In Physical and Theoretical Chemistry
Citations in the corpus
- Machine learning of molecular electronic properties in chemical compound space (2013, neutral): “It includes, for example, the modeling of potential energy surfaces with artificial NNs starting with the work of Sumpter and Noid in 1992 [43–49] or Gaussian processes [50, 51].”