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Inferring transient molecular structural dynamics from diffraction data is anambiguous task that often requires different approximation methods. In thispaper we present an attempt to tackle this problem using machine learning.While most recent applications of machine learning for the analysis ofdiffraction images apply only a single neural network to an experimentaldataset and train it on the task of prediction, our approach utilizes anadditional generator network trained on both synthetic data and experimentaldata. Our network converts experimental data into idealized diffractionpatterns from which information is extracted via a convolutional neural network(CNN) trained on synthetic data only. We validate this approach on ultrafastelectron diffraction (UED) data of bismuth samples undergoing thermalizationupon excitation via 800 nm laser pulses. The network was able to predicttransient temperatures with a deviation of less than 6% from analyticallyestimated values. Notably, this performance was achieved on a dataset of 408images only. We believe employing this network in experimental settings wherehigh volumes of visual data are collected, such as beam lines, could provideinsights into the structural dynamics of different samples.

Novel applications of Generative Adversarial Networks (GANs) in the analysis of ultrafast electron diffraction (UED) images