Accelerating Electronic Stopping Power Predictions by 10 Million Times
  with a Combination of Time-Dependent Density Functional Theory and Machine
  Learning
Logan Ward Ben Blaiszik Cheng-Wei Lee Troy Martin Ian Foster André Schleife
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 time-dependent density functional theory (TDDFT) and
machine learning to reduce the time to assess new materials to mere hours on a
supercomputer and provides valuable data on how atomic details influence
electronic stopping. Our approach uses TDDFT to compute the electronic stopping
contributions to stopping power from first principles in several directions and
then machine learning to interpolate to other directions at rates 10 million
times higher. We demonstrate the combined approach in a study of proton
irradiation in aluminum and employ it to predict how the depth of maximum
energy deposition, the "Bragg Peak," varies depending on incident angle -- a
quantity otherwise inaccessible to modelers. The lack of any experimental
information requirement makes our method applicable to most materials, and its
speed makes it a prime candidate for enabling quantum-to-continuum models of
radiation damage. The prospect of reusing valuable TDDFT data for training the
model make our approach appealing for applications in the age of materials data
science.