  Sustainable aviation fuels have the potential for reducing emissions and
environmental impact. To help identify viable sustainable aviation fuels and
accelerate research, several machine learning models have been developed to
predict relevant physiochemical properties. However, many of the models have
limited applicability, leverage data from complex analytical techniques with
confined spectral ranges, or use feature decomposition methods that have
limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR)
spectra, this study presents a structured method for creating accurate and
interpretable property prediction models for neat molecules, aviation fuels,
and blends. Liquid-phase FTIR spectra measurements can be collected quickly and
consistently, offering high reliability, sensitivity, and component specificity
using less than 2 mL of sample. The method first decomposes FTIR spectra into
fundamental building blocks using Non-negative Matrix Factorization (NMF) to
enable scientific analysis of FTIR spectra attributes and fuel properties. The
NMF features are then used to create five ensemble models for predicting final
boiling point, flash point, freezing point, density at 15C, and kinematic
viscosity at -20C. All models were trained using experimental property data
from neat molecules, aviation fuels, and blends. The models accurately predict
properties while enabling interpretation of relationships between compositional
elements of a fuel, such as functional groups or chemical classes, and its
properties. To support sustainable aviation fuel research and development, the
models and data are available on an interactive web tool.
