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

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
data sets, 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 pseudoternary 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.