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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Leveraging Large Language Models to Optimize Materials Synthesis and Design

Author(s)

Devi Dutta Biswajeet, Sara Kadkhodaei

On-Site Speaker (Planned)

Devi Dutta Biswajeet

Abstract Scope

This work explores how Large Language Models (LLMs) can enhance machine learning for materials synthesis and design, particularly in data-scarce cases. First, we apply LLMs to improve machine learning of chemical vapor deposition (CVD) of graphene on a limited, heterogeneous dataset collected from various studies in the literature. Our approach uses LLM-driven data imputation and LLM-generated embeddings to represent complex substrate nomenclature, boosting support vector machine (SVM) classification accuracy for number of graphene layers from 39% to 65% (binary) and from 52% to 72% (ternary). Besides LLM approaches to overcome data scarcity challenges in materials machine learning, we introduce an LLM-integrated ecosystem that combines language models with knowledge graphs to extract trends and generate novel synthesis hypotheses. Using this system, we map a two-decade roadmap of diamond CVD synthesis, identifying potential pathways for future exploration.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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