  Non-negative Matrix Factorization (NMF) methods offer an appealing
unsupervised learning method for real-time analysis of streaming spectral data
in time-sensitive data collection, such as $\textit{in situ}$ characterization
of materials. However, canonical NMF methods are optimized to reconstruct a
full dataset as closely as possible, with no underlying requirement that the
reconstruction produces components or weights representative of the true
physical processes. In this work, we demonstrate how constraining NMF weights
or components, provided as known or assumed priors, can provide significant
improvement in revealing true underlying phenomena. We present a PyTorch based
method for efficiently applying constrained NMF and demonstrate this on several
synthetic examples. When applied to streaming experimentally measured spectral
data, an expert researcher-in-the-loop can provide and dynamically adjust the
constraints. This set of interactive priors to the NMF model can, for example,
contain known or identified independent components, as well as functional
expectations about the mixing of components. We demonstrate this application on
measured X-ray diffraction and pair distribution function data from $\textit{in
situ}$ beamline experiments. Details of the method are described, and general
guidance provided to employ constrained NMF in extraction of critical
information and insights during $\textit{in situ}$ and high-throughput
experiments.
