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

arXiv: https://arxiv.org/abs/2511.10673 · HTML: https://mlsanity.com/paper/2511.10673/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups](https://mlsanity.com/paper/2608.08883/) (2026)
- [AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research](https://mlsanity.com/paper/2609.16519/) (2026)

## Abstract (arXiv)

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.