Natural language access point to digital metal-organic polyhedra chemistry in The World Avatar
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Date
2025
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Journal Article
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Abstract
Metal-organic polyhedra (MOPs) are discrete, porous metal-organic assemblies known for their wide-ranging applications in separation, drug delivery, and catalysis. As part of The World Avatar (TWA) project-a universal and interoperable knowledge model-we have previously systematized known MOPs and expanded the explorable MOP space with novel targets. Although these data are available via a complex query language, a more user-friendly interface is desirable to enhance accessibility. To address a similar challenge in other chemistry domains, the natural language question-answering system "Marie" has been developed; however, its scalability is limited due to its reliance on supervised fine-tuning, which hinders its adaptability to new knowledge domains. In this article, we introduce an enhanced database of MOPs and a first-of-its-kind question-answering system tailored for MOP chemistry. By augmenting TWA's MOP database with geometry data, we enable the visualization of not just empirically verified MOP structures but also machine-predicted ones. In addition, we renovated Marie's semantic parser to adopt in-context few-shot learning, allowing seamless interaction with TWA's extensive MOP repository. These advancements significantly improve the accessibility and versatility of TWA, marking an important step toward accelerating and automating the development of reticular materials with the aid of digital assistants.
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Volume
6
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Publisher
Cambridge University Press
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Subject
dynamic knowledge graphs; metal - organic polyhedra; question-answering systems; retrieval-augmented generation