3 papers
nucl-th2026
Bayesian Inference for Extracting Barrier Distributions from Fusion Excitation Functions
Aaron Philip, Pablo Giuliani, Kyle Godbey
Barrier distributions encode rich information about the structure and dynamics of fusing nuclei, but extracting them from experimental fusion cross sections requires an estimate of…
nucl-th2026
Extracting Barrier Distributions from Fusion Cross Sections
Aaron Philip
Studying fusion cross sections provides insight into the fusion process, details about the internal structure of heavier nuclear systems, and a window into astrophysical processes.…
physics.chem-ph2022
Using Machine Learning Hamiltonians To Compute Molecular Motor Barrier Heights
Aaron Philip, Guoqing Zhou, Benjamin Nebgen
Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not cle…