You probably have too few points to get a physically significant fit. To get the curvature on the log scale you might try
$$\ln(y)=a\left(\ln(x)\right)^2+b\ln(x)+c,$$
i.e. fitting a parabola to those points, but it's unclear that that will give you anything of value (it gives the model $y=Cx^{b+a\ln(x)}$ which is pretty ugly).
I would say you're doing this the wrong way round: start with physical considerations to derive some kind of model and then do a fit to see if it works. The problem is that there is probably a large number of different curves that will fit those points well enough, and more so if you allow more than three or four parameters. The only way out (and even then I'd say it's a feeble one) is to get a large amount of data with large precision. Then, if a model with only a few parameters fits all your points within their uncertainty, you're on to something that theory has to account for.
It also depends on what you want your fit to do. Do you expect some kind of theoretical insight? then look to theory. Do you want to interpolate your data? then there are ready-made interpolation algorithms (some, but not all, of which include a polynomial fit to the data, though they are not usually least-squares fits to all the data but rather exact fits to part of the data). Do you want some kind of derivative or integral from that curve? then evaluate it numerically. And so on.