  High-Throughput materials discovery involves the rapid synthesis,
measurement, and characterization of many different but structurally-related
materials. A key problem in materials discovery, the phase map identification
problem, involves the determination of the crystal phase diagram from the
materials' composition and structural characterization data. We present
Phase-Mapper, a novel AI platform to solve the phase map identification problem
that allows humans to interact with both the data and products of AI
algorithms, including the incorporation of human feedback to constrain or
initialize solutions. Phase-Mapper affords incorporation of any spectral
demixing algorithm, including our novel solver, AgileFD, which is based on a
convolutive non-negative matrix factorization algorithm. AgileFD can
incorporate constraints to capture the physics of the materials as well as
human feedback. We compare three solver variants with previously proposed
methods in a large-scale experiment involving 20 synthetic systems,
demonstrating the efficacy of imposing physical constrains using AgileFD.
Phase-Mapper has also been used by materials scientists to solve a wide variety
of phase diagrams, including the previously unsolved Nb-Mn-V oxide system,
which is provided here as an illustrative example.
