| الوصف: |
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. |
