A neural network approach to the rapid computation of rotational correlation times from slow motional ESR spectra.
We explore the use of feed forward artificial neural networks for determining rotational correlation times from slow motional nitroxide electron spin resonance spectra. This approach is rapid and potentially eliminates the need for traditional iterative fitting procedures. Two networks are examined: the radial basis network and the multilayer perceptron. Although the radial basis network trains rapidly and performs well on simulated spectra, it is less satisfactory when applied to experimental spectra. In contrast, the multilayer perceptron trains slowly but is excellent at extracting correlation times from experimental spectra. In addition, the multilayer perceptron operates well in the presence of noise as long as the signal-to-noise ratio is greater than approximately 200/1. These findings suggest neural networks offer a promising approach for rapidly extracting correlation times without the need for iterative simulations.
Martinez
G V
GV
Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, California, 95064, USA.
Millhauser
G L
GL
D004578:Electron Spin Resonance Spectroscopy / Q000379:methods*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012399:Rotation