Is Julia going to kill Python? Who knows; probably not. But it’s certainly a fun, modern, and interesting language to use, and especially appealing for scientists with a MATLAB background like me. It’s got a unique combination of features: it’s 1-indexed, dynamically typed but (just-in-time) compiled, has a wonderful built-in package manager, and is a breeze to use interactively (concurrent use of scripting and the REPL feels very much like MATLAB development). I was keen to dive into getting to know native Julia, and despite regularly using PyTorch and Tensorflow I’ve never actually coded up backpropagation “by hand” myself before…

In this article we’ll run through the capabilities of AstroEBSD 2.0 for analysing simultaneously collected EBSD and EDS datasets. We will assume a degree of knowledge about these techniques. Our approach lets you understand the interplay between structure and chemistry within metallurgical precipitates, inclusions, and other features of engineering interest.

We will demonstrate (1) data loading, (2) principal component analysis dataset decompositions, and (3) refined template matching for phase-ID. For (3) you will need Bruker DynamicS EBSP simulations.

We have set up a (hopefully easy to use) PCA workflow deck, so users can input their settings, filepaths to data, etc…

In this piece we consider applying machine learning to a pretty niche field: electron backscatter diffraction. Does it add value? Is it hype? What questions are we better able to ask and answer using modern techniques? Here I don’t delve too much into the maths or physics, or rely too heavily on microscopy concepts — this is meant as a viewpoint on applying ML to a fairly mature domain.

We are probably in a bubble. AI is seeing pretty excessive use in basically every scientific field. Some of it is hype. …

Tom McAuliffe

ML researcher | Microscopy PhD | Engineer

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