A literature-guided descriptor-based framework for filtering composition search spaces
Scientific literature contains latent knowledge about materials behavior, but much of this knowledge is expressed through words, contexts, and recurring associations rather than explicit design principles. This raises a central question: how can large-scale scientific corpora be used for practical problems in materials discovery? Here, we present a literature-guided descriptor-based filtering framework for reducing composition search spaces. For a given performance metric, the framework selects two descriptors from a filtered vocabulary in a literature-trained word embedding model and uses the selected descriptors to construct a Pareto-based filter for candidate compositions. Across the e...
| Authors | Lei Zhang, Markus Stricker |
|---|---|
| Primary category | cs.CL |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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