Synthesis parameter effect detection using quantitative representations and high dimensional distribution distances
Materials Science
2023-04-04 v1 Machine Learning
Machine Learning
Abstract
Detection of effects of the parameters of the synthetic process on the microstructure of materials is an important, yet elusive goal of materials science. We develop a method for detecting effects based on copula theory, high dimensional distribution distances, and permutational statistics to analyze a designed experiment synthesizing plutonium oxide from Pu(III) Oxalate. We detect effects of strike order and oxalic acid feed on the microstructure of the resulting plutonium oxide, which match the literature well. We also detect excess bivariate effects between the pairs of acid concentration, strike order and precipitation temperature.
Cite
@article{arxiv.2304.01120,
title = {Synthesis parameter effect detection using quantitative representations and high dimensional distribution distances},
author = {Alex Hagen and Shane Jackson},
journal= {arXiv preprint arXiv:2304.01120},
year = {2023}
}
Comments
6 pages, 2 figures