  Crystal-structure phase mapping is a core, long-standing challenge in
materials science that requires identifying crystal structures, or mixtures
thereof, in synthesized materials. Materials science experts excel at solving
simple systems but cannot solve complex systems, creating a major bottleneck in
high-throughput materials discovery. Herein we show how to automate
crystal-structure phase mapping. We formulate phase mapping as an unsupervised
pattern demixing problem and describe how to solve it using Deep Reasoning
Networks (DRNets). DRNets combine deep learning with constraint reasoning for
incorporating scientific prior knowledge and consequently require only a modest
amount of (unlabeled) data. DRNets compensate for the limited data by
exploiting and magnifying the rich prior knowledge about the thermodynamic
rules governing the mixtures of crystals with constraint reasoning seamlessly
integrated into neural network optimization. DRNets are designed with an
interpretable latent space for encoding prior-knowledge domain constraints and
seamlessly integrate constraint reasoning into neural network optimization.
DRNets surpass previous approaches on crystal-structure phase mapping,
unraveling the Bi-Cu-V oxide phase diagram, and aiding the discovery of
solar-fuels materials.
