most citedAccelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning

13 citations · 13 across the 1 of their papers we have counts for

collaborators

1 paper

cond-mat.mtrl-sci2023★ 13 cited

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 +3

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…