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Description
Recent advances in large language models (LLMs), foundation models, and AI agents are transforming materials science from AI-assisted characterization toward autonomous scientific discovery. While machine learning has achieved remarkable success in materials property prediction, discovering entirely new functional materials remains challenging because of the enormous chemical design space and the fragmented nature of scientific knowledge distributed across literature and materials databases.
In this work, we present an AI-assisted framework for the autonomous discovery of novel ferroelectric materials by integrating automated literature mining, large language models, structured materials databases, and physics-guided machine learning. We have established a comprehensive ferroelectric materials database containing hundreds of unique compounds and thousands of extracted materials-property records, including crystal structures, spontaneous polarization, Curie temperatures, coercive fields, dielectric constants, piezoelectric coefficients, and other key descriptors. Large language models are employed to automatically extract and organize scientific knowledge from the literature, while materials databases such as the Materials Project provide complementary structural and computational information.
Building upon this knowledge base, we are developing a physics-guided AI discovery pipeline that combines LLM-based knowledge extraction with data-driven screening to identify promising unexplored ferroelectric candidates. Rather than relying solely on black-box prediction, the framework incorporates physical constraints—including crystallographic symmetry, thermodynamic stability, and established ferroelectric mechanisms—to improve both interpretability and prediction reliability.
This work represents a step toward autonomous materials discovery, where AI assists not only in analyzing experimental data but also in generating hypotheses and identifying new candidate materials for computational and experimental validation. More broadly, the framework provides a scalable approach for accelerating the discovery of functional quantum materials by integrating scientific literature, materials databases, machine learning, and physics-based reasoning.