  Accurate, high throughput, and unbiased analysis of plutonium oxide particles
is needed for analysis of the phenomenology associated with process parameters
in their synthesis. Compared to qualitative and taxonomic descriptors,
quantitative descriptors of particle morphology through scanning electron
microscopy (SEM) have shown success in analyzing process parameters of uranium
oxides. We utilize a VQ-VAE to quantitatively describe plutonium dioxide (PuO2)
particles created in a designed experiment and investigate their phenomenology
and prediction of their process parameters. PuO2 was calcined from Pu(III)
oxalates that were precipitated under varying synthetic conditions that related
to concentrations, temperature, addition and digestion times, precipitant feed,
and strike order; the surface morphology of the resulting PuO2 powders were
analyzed by SEM. A pipeline was developed to extract and quantify useful image
representations for individual particles with the VQ-VAE to perform multiple
classification tasks simultaneously. The reduced feature space could predict
process parameters with greater than 80% accuracies for some parameters with a
single particle. They also showed utility for grouping particles with similar
surface morphology characteristics together. Both the clustering and
classification results reveal valuable information regarding which chemical
process parameters chiefly influence the PuO2 particle morphologies: strike
order and oxalic acid feedstock. Doing the same analysis with multiple
particles was shown to improve the classification accuracy on each process
parameter over the use of a single particle, with statistically significant
results generally seen with as few as four particles in a sample.
