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Rapid construction of phase diagrams is a central tenet of combinatorialmaterials science with accelerated materials discovery efforts often hamperedby challenges in interpreting combinatorial x-ray diffraction datasets, whichwe address by developing AgileFD, an artificial intelligence algorithm thatenables rapid phase mapping from a combinatorial library of x-ray diffractionpatterns. AgileFD models alloying-based peak shifting through a novel expansionof convolutional nonnegative matrix factorization, which not only improves theidentification of constituent phases but also maps their concentration andlattice parameter as a function of composition. By incorporating Gibbs phaserule into the algorithm, physically meaningful phase maps are obtained withunsupervised operation, and more refined solutions are attained by injectingexpert knowledge of the system. The algorithm is demonstrated throughinvestigation of the V-Mn-Nb oxide system where decomposition of eight oxidephases, including two with substantial alloying, provides the first phase mapfor this pseudo-ternary system. This phase map enables interpretation ofhigh-throughput band gap data, leading to the discovery of new solar lightabsorbers and the alloying-based tuning of the direct-allowed band-gap energyof MnV2O6. The open-source family of AgileFD algorithms can be implemented intoa broad range of high throughput workflows to accelerate materials discovery.

Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System