Large language models in materials science and the need for open-source approaches

• URL: http://arxiv.org/abs/2511.10673v1
• Date: Mon, 10 Nov 2025 00:05:20 GMT
• Title: Large language models in materials science and the need for open-source approaches
• Authors: Fengxu Yang, Weitong Chen, Jack D. Evans,
• Abstract summary: Review examines recent large language models (LLMs) applications across the materials discovery pipeline.<n>We highlight how LLMs extract valuable information such as synthesis conditions from text.<n> benchmark results demonstrate that open-source alternatives can match performance while offering greater transparency, cost-effectiveness, and data privacy.<n>As open-source models continue to improve, we advocate their broader adoption to build accessible, flexible, and community-driven AI platforms for scientific discovery.
• Score: 3.35950184561189
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) are rapidly transforming materials science. This review examines recent LLM applications across the materials discovery pipeline, focusing on three key areas: mining scientific literature , predictive modelling, and multi-agent experimental systems. We highlight how LLMs extract valuable information such as synthesis conditions from text, learn structure-property relationships, and can coordinate agentic systems integrating computational tools and laboratory automation. While progress has been largely dependent on closed-source commercial models, our benchmark results demonstrate that open-source alternatives can match performance while offering greater transparency, reproducibility, cost-effectiveness, and data privacy. As open-source models continue to improve, we advocate their broader adoption to build accessible, flexible, and community-driven AI platforms for scientific discovery.

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