Hamiltonian Monte Carlo Markov Chain (HMCMC) modeling is a statistical sampling method that allows for the efficient sampling of a conditional probability distribution when only a joint probability distribution is available. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, it could be possible to use HMCMC to fit DFT-predicted spectra to crude reaction spectra. In most cases, a synthetic chemist knows which products or side products are likely to have formed in a reaction and can provide structures of all relevant products. It is also becoming more common for reaction-prediction models to enumerate probable reaction products. One could imagine creating a tool that considers a list of products provided by a user, identifies which products are in the crude spectrum, and determines their relative concentrations. This approach relies on the use of computed spectra in place of experimentally determined spectra of authentic products, thereby dramatically increasing the utility by enabling the identification and quantification of products in a reaction mixture that have not been previously described.