Abstract Important physical and chemical information can be extracted from scattering experiments data. This kind of problem is usually ill‐posed in the sense that one of the three conditions, existence, uniqueness, and continuity, is not satisfied. For example, the inversion of intermolecular potential functions from scattering data, such as experimental cross section, is an ill‐posed problem which can be modeled as a Fredholm integral equation. In this work, an inversion method based on recursive neural networks is proposed to solve this inverse quantum scattering problem within the Born approximation. As physical example, the repulsive component of the potential function for the interaction Ar–Ar is obtained from differential cross‐section data. The sensitivity of the potential energy function to be inverted, in relation to the differential cross‐section data, is also analyzed. The present approach is simple, general, and numerically stable. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008
Authors: Nelson H. T. Lemes, Érica Rievrs Borges, R.V. Sousa, J. P. Braga
DOI: https://doi.org/10.1002/qua.21701
Publish Year: 2008
