  Recent innovations in x-ray technology (namely phase-based and
energy-resolved imaging) offer unprecedented opportunities for material
discrimination, however they are often used in isolation or in limited
combinations. Here we show that the optimized combination of contrast channels
(attenuation at three x-ray energies, ultra-small angle scattering at two,
standard deviation of refraction) significantly enhances material
identification abilities compared to dual-energy x-ray imaging alone, and that
a combination of off-the-shelf machine learning approaches can effectively
discriminate e.g., threat materials in complex datasets. The methodology is
validated on a range of materials and image dataset that are both an order of
magnitude larger than those used in previous studies. Our results can provide
an effective methodology to discriminate, and in some cases identify, different
materials in complex imaging scenarios, with prospective applications across
the life and physical sciences. While the detection of threat materials is used
as a demonstrator here, the methodology could be equally applied to e.g., the
distinction between diseased and healthy tissues or degraded vs. pristine
materials.
