In the modern age of data science, there is more catalogued and query-able materials data than ever before. We employ high-throughput computational materials discovery techniques to survey uncharted chemical spaces for novel synthesizable materials, constructing large stability maps to help guide exploratory synthesis. Using data-mining and machine-learning algorithms, we aim to explain the complex interplay between chemistry, composition, and electronic structure in governing large-scale stability trends across broad materials spaces.
A Map of the Inorganic Ternary Metal Nitrides
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