An Autonomous Scientific Knowledge Generation Framework for AI-Driven Scientific Discovery
Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge. Although existing databases have accelerated data-driven materials research, much of the knowledge needed for predictive modeling and inverse design remains embedded in unstructured scientific literature. We present an Autonomous Scientific Knowledge Generation Framework that transforms scientific publications into a Unified AI-Ready Scientific Knowledge Base. The framework integrates ontology-guided literature acquisition, hybrid scientific knowledge extraction, semantic harmonization, knowledge fusion, and validation wi...
| Authors | Dibakar Datta |
|---|---|
| Primary category | cs.DL |
| Published | 9 July 2026 |
| Last updated at source | 3 August 2026 |
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