I’m delighted to announce that Jun Yang has successfully defended his PhD thesis, “Machine Learning for Electronic and Atomistic Simulations.” Jun’s research centered on improving computational approaches in materials science and quantum chemistry, notably by developing a machine learning interatomic potential that enhances both simulation accuracy and efficiency while lowering computational demands. He also applied machine learning techniques to time-dependent density functional theory, advancing our understanding of how to measure the Kohn-Sham system. We wish Jun continued success as he transitions into the tech industry at Amazon Web Services.
Dartmouth | Department of Physics and Astronomy