Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning. (arXiv:2311.00787v1 [cond-mat.mtrl-sci])

Knowing the rate at which particle radiation releases energy in a material,
the stopping power, is key to designing nuclear reactors, medical treatments,
semiconductor and quantum materials, and many other technologies. While the
nuclear contribution to stopping power, i.e., elastic scattering between atoms,
is well understood in the literature, the route for gathering data on the
electronic contribution has for decades remained costly and reliant on many
simplifying assumptions, including that materials are isotropic. We establish a
method that combines …

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