Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques

Abstract

In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from DoerkG. S. Doerk, G. S. eadd3687Sci. Adv.20239], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample’s corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure–property relationships.

Deep Learning Analysis of the Propagation of Stabilizing Additive Hydrolysis in a Cross-Linked Polyethylene Pipe

“…One of the challenging aspects of using IR microscopy to study the spatial distribution of IR spectra of different samples aged under different environmental conditions is understanding the large amount of data that is generated. Instead of using a univariate analysis to focus on specific IR absorption bands to interpret these large amounts of data as has been done in previous studies, ,− machine learning approaches such as deep or generative learning have been used recently in the field of materials science to provide new insights into the analysis of large amounts of spectroscopy and microscopy data. − Recently, we developed a deep learning β-variational autoencoder (β-VAE) model to analyze IR spectra of PEX-a pipe. , β-VAEs are a class of deep generative models that can learn disentangled (independent and interpretable) representations of the generative factors responsible for variance in data. − We previously trained our β-VAE model on a diverse data set of 25,000 IR spectra of PEX-a pipe exposed to different environmental conditions . We learned three informative latent dimensions corresponding to three physicochemical processes responsible for the variance in the IR microscopy data: the most informative latent dimension, L1, which describes the hydrolysis of a stabilizing additive; the second latent dimension, L2, which describes the oxidative degradation of amorphous polyethylene; and the third latent dimension, L3, which describes crack-specific degradation characterized by ketone carbonyl and conjugated alkene formation.…”