[Ifeffit] Normalization of LCF coefficients when fitting experimental spectra with theoretical spectra
mmassey at gmail.com
Wed Mar 10 11:42:34 CST 2021
When you say "worse than the initial one," what do you mean?
If it's a "worse" match than nonsensical fit results, but makes some physical and chemical sense, I'd personally argue it's a much better fit ;)
What you are doing seems reasonable to me, but I would further recommend keeping your fit component selection as simple as possible.
I'm a big proponent of two-component fits, maybe three if there's a really good reason. I feel like a lot of people do multicomponent (3-4 fitting spectra, maybe even more) LCF with the argument that the inclusion of additional components improves the fit statistics...But to me that gives a false sense of certainty that the technique doesn't really allow (at least for the systems I work with).
For reporting qualitative trends, it seems like you're on the right track. Perhaps unless it's a qualitative trend of a minor component that might not even be there at all and you're basing your entire argument on it definitely being there, which uhh. Seems to happen a lot.
> On Mar 11, 2021, at 1:42 AM, Patricia Poths <patriciapoths at chem.ucla.edu> wrote:
> I am a theoretical chemistry PhD student working on fitting experimental spectra with computed spectra in order to get a better understanding of the composition. In the LCF process with athena, I have found that when I allow "sum coefficients to 1", I get an unphysical negative coefficient of the last standard- after reading through the mailing list I understood why, and so no longer use that. The sum of my coefficients during the fitting now is close to 1- generally within the range of 0.95-1.1 at the absolute extremes, but more often around ~0.98- ~1.05.
> In order to compare these coefficients, I renormalize them to 1, so they can represent the fractions of each component present. However, to test this I took the new normalized coefficients and summed up the standards with their respective weights to create this normalized "fit", and found that it is worse than the initial one. Is this something I should be concerned about when reporting the qualitative trends in how the composition changes? And if so, is there a better way to do the fits in a more normalized way?
> Many thanks,
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