  Rapid construction of phase diagrams is a central tenet of combinatorial
materials science with accelerated materials discovery efforts often hampered
by challenges in interpreting combinatorial x-ray diffraction datasets, which
we address by developing AgileFD, an artificial intelligence algorithm that
enables rapid phase mapping from a combinatorial library of x-ray diffraction
patterns. AgileFD models alloying-based peak shifting through a novel expansion
of convolutional nonnegative matrix factorization, which not only improves the
identification of constituent phases but also maps their concentration and
lattice parameter as a function of composition. By incorporating Gibbs phase
rule into the algorithm, physically meaningful phase maps are obtained with
unsupervised operation, and more refined solutions are attained by injecting
expert knowledge of the system. The algorithm is demonstrated through
investigation of the V-Mn-Nb oxide system where decomposition of eight oxide
phases, including two with substantial alloying, provides the first phase map
for this pseudo-ternary system. This phase map enables interpretation of
high-throughput band gap data, leading to the discovery of new solar light
absorbers and the alloying-based tuning of the direct-allowed band-gap energy
of MnV2O6. The open-source family of AgileFD algorithms can be implemented into
a broad range of high throughput workflows to accelerate materials discovery.
