Lately, there have been some efforts to incorporate machine learning in experimental measurements, which are generally quite known in the community, and especially the quantum one (see here for example). While these types of work are currently ‘hot’, I decided to do a small post here about the small cousin of ML, which is automation. That is: Extracting information from large datasets of experiments.
This came about from my recently published work done at Grenoble, in which I had the chance to work with a large number of well-organized experiments. And I think it goes nicely with my previous post which is about automation in materials simulation.
Here, instead, I will present some common methods of extracting pinch-off voltages using Python. I did a previous post on a similar subject. Together they can be quite handy for extracting information fast from 1D data. Of course, they can be generalized for 2D also, but the here we focus on device measurements and not spectroscopy. In fact, for the 2D plots I analysed, I handled them as a list of 1D data, so I applied immediately similar routines, instead of 2D ones.Continue reading