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This research applies natural language processing techniques to extract latent knowledge from scientific literature, using word embeddings to filter materials composition search spaces effectively. By automating the selection of performance descriptors, the framework reduces candidate compositions by approximately 74% while maintaining high accuracy relative to experimental data. This approach demonstrates a practical intersection of AI and quantum computing where machine learning accelerates material discovery for quantum hardware applications.
A literature-guided descriptor-based framework for filtering composition search spaces
arXiv:2609.31650v1 Announce Type: new Abstract: 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 evaluated performance metrics and composition search spaces, the framework filters out an average of 74.27\% of the candidate compositions, with an average best-value error of 1.93\% relative to experimental measurements. Compared with expert-chosen and random descriptors, our performance metric-dependent descriptors provide a more controlled balance between retained fraction and best-value error. These results show that literature-derived embeddings can support intuitive and reproducible filters for narrowing candidate composition spaces while preserving high-performing compositions.
Abstract: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 evaluated performance metrics and composition search spaces, the framework filters out an average of 74.27\% of the candidate compositions, with an average best-value error of 1.93\% relative to experimental measurements. Compared with expert-chosen and random descriptors, our performance metric-dependent descriptors provide a more controlled balance between retained fraction and best-value error. These results show that literature-derived embeddings can support intuitive and reproducible filters for narrowing candidate composition spaces while preserving high-performing compositions.
Computation and Language (cs.CL); Materials Science (cond-mat.mtrl-sci)
arXiv:2609.31650 [cs.CL]
(or arXiv:2609.31650v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.31650 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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