Applying Neural Networks and Gaussian Process Regression to the Transition Structure Factor

We explore two machine learning algorithms for analyzing the transition structure factor based on coupled cluster doubles calculations on the uniform electron gas. First, we use Gaussian process regression to complete the transition structure factor curve for a range of electron numbers. We then integrate and extrapolate for the thermodynamic limit correlation energy. Second, we use neural networks to explore transfer learning with the transition structure factor. The potential for using relatively simple systems to attain information about large systems is indicated by both machine learning applications.