Publications
Theoretical Nuclear Physics and Machine Learning
Accelerating the Convergence of Coupled Cluster Calculations of the Homogeneous Electron Gas Using Bayesian Ridge Regression
Authors: Julie Butler, Morten Hjorth-Jensen, Justin Lietz
Abstract: The homogeneous electron gas is a system which has many applications in chemistry and physics. However, its infinite nature makes studies at the many-body level complicated due to long computational run times. Because it is size extensive, coupled cluster theory is capable of studying the homogeneous electron gas, but it still poses a large computational challenge as the time needed for precise calculations increases in a polynomial manner with the number of particles and single-particle states. Consequently, achieving convergence in energy calculations becomes challenging, if not prohibited, due to long computational run times and high computational resource requirements. This paper develops the sequential regression extrapolation (SRE) to predict the coupled cluster energies of the homogeneous electron gas in the complete basis limit using Bayesian ridge regression to make predictions from calculations at truncated basis sizes. Using the SRE method we were able to predict the coupled cluster doubles energies for the electron gas across a variety of values of N and rₛ, for a total of 70 predictions, with an average error of 4.28x10⁻⁴ Hartrees while saving 88.9 hours of computational time. The SRE method can accurately extrapolate electron gas energies to the complete basis limit, saving both computational time and resources. Additionally, the SRE is a general method that can be applied to a variety of systems, many-body methods, and extrapolations.
Computing in Physics Education
Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice
Authors: Karan Shah, Julie Butler, Alexis Knaub, Anıl Zenginog ̆lu, William Ratcliff, Mohammad Soltanieh-ha
Abstract: With the increasing availability of diverse datasets, ranging from small-scale experimental data points to large and complex data repositories and powerful data analysis tools, it is increasingly important that physics educators equip their students with the skills to work with data effec- tively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To address this gap, we created the Data Science Education Community of Practice (DSECOP), bringing together graduate students and physics educators from different institutions and backgrounds to share best practices and lessons learned in integrating data science into undergraduate physics education. In this article, we present insights and experiences from this community of practice, highlighting key strategies and challenges in incorporating data science into the introductory physics curriculum. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world.