Synthesis parameter effect detection using quantitative representations and high dimensional distribution distances
This addresses a critical but challenging goal in materials science for researchers, though it appears incremental as it builds on existing statistical techniques for a specific domain.
The researchers tackled the problem of detecting how synthesis parameters affect material microstructure by developing a method based on copula theory and high-dimensional distribution distances, applying it to plutonium oxide synthesis and detecting effects of strike order and oxalic acid feed that align with literature, as well as excess bivariate effects among parameters.
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.