A nested molecule-independent neural network approach for high-quality potential fits.

By Sergei Manzhos, Xiaogang Wang, Richard Dawes, Tucker Carrington Jr.. 2006

176 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Materials 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

1. Matthew J. Hirn
2. Stéphane Mallat
3. Nicolas Poilvert

In Materials Chemistry

1. Stéphane Mallat
2. Matthew J. Hirn
3. Nicolas Poilvert
4. Risi Kondor

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].”
• Wavelet Scattering Regression of Quantum Chemical Energies (2016, background): “Such algorithms for potential energy surface fitting of one molecule or material have been developed for more than two decades, tracing back to [45] with further contributions in [32, 8, 7, 42, 5, 49].”