Author

Dibakar Datta

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Jul 2026

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 within a unified workflow. Rather than treating literature retrieval, information extraction, and database construction as separate tasks, the framework progressively converts scientific publications into structured, semantically consistent, and provenance-preserving knowledge suitable for AI-driven reasoning. As a proof of concept, the framework was applied to electro-optic materials. Autonomous literature acquisition retrieved and validated about 1,000 publications from multiple scholarly repositories. A representative subset of eight publications was processed through the complete workflow, generating 29 structured scientific records that were harmonized into 7 canonical scientific records. The results demonstrate the complete transformation from scientific literature to an AI-ready scientific knowledge base while preserving quantitative measurements, operating conditions, provenance, and scientific context. The proposed framework provides a scalable, domain-independent foundation for predictive AI, generative AI, and closed-loop AI-driven scientific discovery.

Dibakar Datta · 0 citations

Preprint Aug 2026

Physics-Informed and Knowledge-Driven Generative AI for Autonomous Discovery of Porous Oxide Energy Materials: Opportunities and Challenges

The discovery of next-generation energy-storage materials is increasingly limited by the complexity of the underlying design problem rather than by computational capability alone. Porous transition-metal oxides represent a particularly challenging class of battery materials because their performance emerges from coupled interactions among crystal chemistry, pore architecture, ion transport, electrochemistry, electro-chemo-mechanics, synthesis, manufacturing, and battery-system operation. Recent advances in generative artificial intelligence (AI) have demonstrated remarkable capabilities for generating chemically plausible crystal structures. However, current approaches remain largely focused on crystallographic validity and thermodynamic stability. This perspective presents a roadmap for advancing generative AI beyond crystal generation toward physics-informed, application-aware, and synthesis-aware inverse design. Using porous oxide electrodes as a representative materials platform, we propose a seven-tier physics-informed inverse-design framework integrating chemistry, thermodynamics, transport, electrochemistry, durability, cell compatibility, and manufacturability. We further identify the"Missing Data Problem"as a fundamental bottleneck limiting application-aware AI and introduce an autonomous knowledge-generation framework supported by a Porous Oxide Energy Materials Ontology and a continuously evolving"Knowledge Base". Together, these concepts establish the foundation for Synthesis-Aware, Closed-Loop Autonomous Discovery, providing a general framework for AI-enabled autonomous materials discovery across energy-storage materials and other functional materials.

Dibakar Datta · 0 citations