  Electron emission from cathodes in high field gradients is a quantum
tunneling effect. The Fowler-Nordheim (FN) equation has traditionally been key
in describing cold-field emissions, offering estimates for emitters for almost
a century. Nevertheless, applying FN theory in practice is often constrained by
the lack of data on the distribution and geometry of the emission sites.
Predictions become more challenging with an uneven electric field distribution
at the cathode surface. Consequently, FN formulations are frequently calibrated
using current-voltage data after test, reducing their effectiveness as truly
predictive models.
  This study proposes the development of a data-informed predictive model of
field emission that leverages (1) vast experimental data, (2) electrostatic
simulations of the cathode surface, and (3) detailed material and geometric
properties to overcome these limitations. The goal is to harness this
comprehensive dataset to train a machine learning model capable of providing
precise predictions of the cathode current, thereby enhancing the understanding
and application of field emission phenomena. More than 259 hours of
experimental data have been processed to train and benchmark some of the
well-known machine learning models. After two stages of optimization, a
coefficient of determination $>98\%$ is achieved in the prediction total field
emission current using ensemble models.
