Dear Graeme,
Right, but you are talking about weights that reflect the data quality and say
nothing about that of the starting model ; however refinement is a comparison
of a model with data.
The higher resolution of the data, the more sensitive they to model
imperfections.
Refinement targets are sums over reflections, and each refinement term is a
function with multiple minima; the higher the resoluion, the more frequent
these minima.
If the starting model is too far from the answer, a presence of high-resolution
data prevents the refinement from moving the model as far as necessary; it is
trapped by multiple local minima of the crystallographic functions that include
such high-resolution terms. Removing such terms removes or at least attenuate
the intermediate local minima and improves the convergence. One does not care
about the statistices but about convergence (" the model stops improving"
further than with these data). Increaing the resolution step-by-step was the
standard refinement strategy till the end of 90ths.
Right, using ML-based targets introduced weights based on comparison of Fmodel
with Fobs and allowed to do such attenuation in a "soft way". This was great
and indeed replaced the "before-ML refinement strategy". However, such an
artificial cut-off of highest-resolution data (temporary, at early refinement
stages) can be useful in some situations even now and can improve convergence
even with the modern tools. First cycles of a rigid-body refinement can be an
example.
Another reason for a (temporary) removing of higher-resolution data is a heavy
(systematic) incompleteness of data in the higher-resolution shells.
With best regards,
Sacha
----- Le 5 Juil 19, à 8:05, graeme.win...@diamond.ac.uk
<graeme.win...@diamond.ac.uk> a écrit :
> Pavel,
> Please correct if wrong, but I thought most refinement programs used the
> weights
> e.g. sig(I/F) with I/F so would not really have a hard cut off anyway? You’re
> just making the stats worse but the model should stay ~ the same (unless you
> have outliers in there)
> Clearly there will be a point where the model stops improving, which is the
> “true” limit…
> Cheers Graeme
> On 5 Jul 2019, at 06:49, Pavel Afonine
> <pafon...@gmail.com<mailto:pafon...@gmail.com>> wrote:
> Hi Sam Tang,
> Sorry for a naive question. Is there any circumstances where one may wish to
> refine to a lower resolution? For example if one has a dataset processed to 2
> A, is there any good reasons for he/she to refine to only, say 2.5 A?
> yes, certainly. For example, when information content in the data can justify
> it.. Randy Read can comment on this more! Also instead of a hard cutoff using
> a
> smooth weight based attenuation may be even better. AFAIK, no refinement
> program can do this smartly currently.
> Pavel
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