X-ray coherent diffraction-based measurements can provide more specific material information that is complementary to transmission-based material information. With increasing capability of the X-ray coherent diffraction-based systems and recent development of dual modality of X-ray transmission-based and diffraction-based systems, there is a significant potential for improving the overall system threat detection performance for material discrimination. Dual modality systems can yield higher detection probability (Pd) while lowering the probability of false alarm (Pfa), relative to the transmission modality. In this work, we analyze the material discrimination performance for two different machine learning classifiers: support vector machines (SVM) and neural networks (NN), using both simulation and experimental data obtained with a dual-modality X-ray system. Using simulation studies, we demonstrate significant improvement in material discrimination performance afforded by additional complementary information by coherent diffraction for a variety of materials. We further validate these improvements using an experimental dataset collected using real-world objects and materials.