Using neural networks to represent potential surfaces as sums of products.

By Sergei Manzhos, Tucker Carrington Jr.. 2006

162 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. B. A. Le
2. Qi-Chang He
3. Julien Yvonnet

In Materials Chemistry

1. B. A. Le
2. Qi-Chang He
3. Julien Yvonnet

Citations in the corpus

• Computational homogenization of nonlinear elastic materials using neural networks (2015, methodology): “In [33], exponential functions have been compared with sigmoid functions in NN applications.”