Hi List, It appears that changing the k-weight on my fit does improve the fit. I still have the problem of forcing the sO^2's on the two MS paths to be positive. Here's some new data with k-weight 3: Thanks, Dan Project title : Fitting merge 10_15_04.chi ============================================================ Independent points = 28.581054688 Number of variables = 13.000000000 Chi-square = 1527.118835332 Reduced Chi-square = 98.011262136 R-factor = 0.025169471 Measurement uncertainty (k) = 0.000185138 Measurement uncertainty (R) = 0.081594539 Number of data sets = 1.000000000 Guess parameters +/- uncertainties (initial guess): e0 = 1.1335810 +/- 1.3393990 (guessed as 1.133581 (1.339426)) ss = 0.0035870 +/- 0.0003110 (guessed as 0.003587 (0.000311)) ss2 = 0.0042850 +/- 0.0002780 (guessed as 0.004285 (0.000278)) ss3 = 0.0303100 +/- 0.0510420 (guessed as 0.030310 (0.051043)) ss4 = 0.0000000 +/- 0.0303840 (guessed as 0.000000 (0.030385)) ss5 = -0.0008090 +/- 0.0043530 (guessed as -0.000809 (0.004353)) ss6 = 0.0059560 +/- 0.0089810 (guessed as 0.005956 (0.008981)) alpha= -0.0006040 +/- 0.0023740 (guessed as -0.000604 (0.002374)) alpha2= 0.0073900 +/- 0.0016520 (guessed as 0.007390 (0.001652)) alpha3= -0.0167250 +/- 0.0535940 (guessed as -0.016725 (0.053595)) alpha4= -0.0120340 +/- 0.0773040 (guessed as -0.012034 (0.077302)) alpha5= -0.0216900 +/- 0.0127530 (guessed as -0.021690 (0.012751)) alpha6= 0.0219980 +/- 0.0183950 (guessed as 0.021998 (0.018395)) Def parameters: delr_1 = -0.0027560 delr_2 = 0.0337230 delr_3 = -0.0763210 delr_4 = -0.0549150 delr_5 = -0.0989780 delr_6 = 0.1003830 Set parameters: amp1 = .7 Correlations between variables: e0 and alpha --> 0.8490 e0 and alpha2 --> 0.8006 ss3 and alpha4 --> 0.7123 alpha and alpha2 --> 0.6792 ss4 and alpha5 --> 0.6298 ss and ss2 --> 0.6114 alpha5 and alpha6 --> -0.5959 ss5 and alpha4 --> 0.5328 ss3 and ss4 --> 0.5300 ss5 and ss6 --> -0.4931 ss4 and alpha3 --> -0.4617 ss2 and alpha5 --> 0.3847 ss6 and alpha6 --> 0.3700 ss2 and alpha3 --> 0.3276 ss4 and ss5 --> -0.2764 alpha2 and alpha4 --> 0.2506 All other correlations are below 0.25
Hi Dan, A couple of quick thoughts, that might also be helpful to people starting out with EXAFS fitting. First of all, the good news is that changing the k-weight does not seem to affect the values ifeffit finds for the parameters significantly (i.e. I didn't see any that moved outside of their uncertainties). That's very reassuring. The fact that the fit quality is noticeably better at k-wt 3 suggests to me that you might try raising kmin a bit--maybe the k-wt 3 fit works better because the fits are struggling at the low end of the k-range? Sometimes it's hard to get the background to work right down at low k, and it looks like you have plenty of data range based on the number of independent points. Second, I don't like forcing S02 to 0.7 because 0.5 is too low very much. An iffy sample or problems with normalization can both suppress S02 below the nominally ideal value. Forcing the fit to adopt a higher S02 then just has the effect of screwing up correlated variables, like the Debye-Waller factors. I'd look for explanations for the low S02 rather than just disallowing it. (I certainly have set S02 in some of my published work, but those were cases of complex fits where it seemed to be a false minimum, rather than a sample or normalization error.) Finally, I'd try some "reasonable" constraints on your MS paths. If they are triangle paths, it is not possible to come up with a rigorous formula for how to write the debye-waller factors and alpha's for MS paths in terms of the direct scattering paths, because you don't know for sure how the motions of nearby atoms correlate. But in my experience, which is mainly with metals and transition metal oxides, fits are generally not particularly sensitive to the details of MS path constraint strategies. Here's a constraint strategy of medium complexity that I teach my undergrads that you can try: Def the alpha's of the MS paths to be the average of the alpha's for the direct scattering paths that make them up, weighted by the length of the segment. It's quite probable in a material such as yours that some segment will be a S-S path which you don't have available as a direct scattering path. Maybe just leave that piece out of the average. Def the ss's of the MS paths to be the sum of the ss's for the direct scattering paths that make them up, divided by 4. Why divided by 4? Because the ss for a half-path is rigorously equal to 1/4 the ss for a whole path. Why add them? If the ss of each segment is truly uncorrelated, then adding them is statistically proper. Of course, this is a truly awful approximation in some cases, notably as the paths come closer to being focused (i.e. one of the angles of the triangle becomes large). In cases where the biggest angle in the triangle is more than about 140 degrees, I just treat it as a focused path, but I doubt that's true for your two MS's. Please understand that this constraint scheme is fairly arbitrary! When working with a new material, I suggest trying a constraint scheme like this, and then seeing how sensitive the fit is to this constraint. For example, multiply all the ss's of the MS's paths by 2 and see how stable the fit is. Go back to allowing the alpha for the MS paths to vary freely and see again if the fit is stable. This kind of probing will tell you whether the MS paths are something that have to be carefully considered, or if they are just a small correction to your fit that should be included but don't have to be included particularly carefully. It is my experience that the latter is often the case, but you have to find out for yourself on this material. --Scott Calvin Sarah Lawrence College At 08:20 AM 11/11/2004 -0800, you wrote:
Hi List,
It appears that changing the k-weight on my fit does improve the fit. I still have the problem of forcing the sO^2's on the two MS paths to be positive. Here's some new data with k-weight 3:
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Scott Calvin