One of the biggest challenges facing standoff chemical detection is the ability to accurately predict spectral influences on chemical signatures that are caused by factors such as particle size, particle shape, deposition thickness, or mesoscale crystallinity and morphology. Currently, most chemical detection algorithms treat each substrate/sorbate combination as a separate library entry that must be empirically measured. Physics-based or semi-empirical models work fairly well for fitting spectra and qualitatively mapping features and trends, but are much less accurate for quantitatively predictions, and are too computationally intensive for real-time development of field libraries for practical instruments. IARPA’s MORGOTH’S CROWN prize challenge was a crowdsourced effort to encourage new approaches to infrared spectral modeling to quantitatively predict trace spectra on surfaces from bulk reflectance spectra. Challenge participants were given a training set which included reflectance spectra of sample coupons with trace chemical residues on them, including examples with different particle sizes, crystal structures, and mass loadings. Participants were then asked to generate an algorithm to predict what the spectra of different combinations of chemicals and substrates would look like. The results of the MORGOTH’S CROWN challenge showed that machine-learning based algorithms were better able to quantitatively predict new spectra than physics-based models. The article will describe the execution of the MORGOTH’S CROWN challenge, and discuss the “bigger picture” of what the MORGOTH’S CROWN results mean, and where we “go from here”.