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

TL;DR

This paper introduces a machine learning approach using a generator and CNN to analyze ultrafast electron diffraction images, accurately predicting transient temperatures from limited experimental data.

Contribution

It presents a novel GAN-based framework that converts experimental diffraction data into idealized patterns for improved analysis, outperforming traditional single-network methods.

Findings

Predicted transient temperatures with less than 6% deviation.

Achieved accurate analysis with only 408 images.

Demonstrated potential for high-volume experimental data analysis.

Abstract

Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper we present an attempt to tackle this problem using machine learning. While most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an experimental dataset and train it on the task of prediction, our approach utilizes an additional generator network trained on both synthetic data and experimental data. Our network converts experimental data into idealized diffraction patterns from which information is extracted via a convolutional neural network (CNN) trained on synthetic data only. We validate this approach on ultrafast electron diffraction (UED) data of bismuth samples undergoing thermalization upon excitation via 800 nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples.