  The radiative response of any object is governed by a surface parameter known
as emissivity. Tuning the emissivity of surfaces has been of great interest in
many applications involving thermal radiation such as thermophotovoltaics,
thermal management systems, and passive radiative cooling. Although several
surface engineering techniques (e.g., surface functionalization) have been
pursued to alter the emissivity, there exists a knowledge gap in precisely
predicting the emissivity of a surface prior to the modification/fabrication
process. Predicting emissivity by a physics-based modeling approach is
challenging due to surface's contributing factors, complex interactions and
interdependence, and measuring the emissivity requires a tedious procedure for
every sample. Thus, a new approach is much-needed to systematically predict the
emissivity and expand the applications of thermal radiation. In this work, we
demonstrate the immense advantage of employing artificial intelligence (AI)
techniques to predict the emissivity of complex surfaces. For this aim, we
fabricated 116 bulk aluminum 6061 samples with various surface characteristics
using femtosecond laser surface processing (FLSP). A comprehensive dataset was
established by collecting surface characteristic data, laser operating
parameters, and measured emissivities for all samples. We demonstrated the
application of AI in two distinct scenarios. First, the range of emissivity of
an unknown sample was shown to be estimated correctly solely based on its 3D
surface morphology image. Second, the emissivity of a sample was precisely
predicted based on its surface characteristics data and fabrication parameters.
The implementation of the AI techniques resulted in the highly accurate
prediction of emissivity by showing excellent agreement with the measurements.
