# DARWIN 1.5: Large Language Models as Materials Science Adapted Learners

arXiv: https://arxiv.org/abs/2412.11970 · HTML: https://mlsanity.com/paper/2412.11970/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2024.

## Built on by (most cited first, 9 of 9)

- [Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.10708/) (2025)
- [Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning](https://mlsanity.com/paper/2502.02871/) (2025)
- [ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models](https://mlsanity.com/paper/2505.12534/) (2025)
- [TopoMAS: Large Language Model Driven Topological Materials Multiagent System](https://mlsanity.com/paper/2507.04053/) (2025)
- [AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials](https://mlsanity.com/paper/2510.04704/) (2025)
- [Training-Free Active Learning Framework in Materials Science with Large Language Models](https://mlsanity.com/paper/2511.19730/) (2025)
- [From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2605.03205/) (2026)
- [SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models](https://mlsanity.com/paper/2601.22312/) (2026)
- [Discovering physical mechanisms from experiment-simulation mismatches](https://mlsanity.com/paper/2604.26703/) (2026)

## Abstract (arXiv)

Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as high-throughput simulations or machine learning, often rely on complex descriptors, which hinder generalizability and transferability across different material systems. Moreover, These descriptors may inadequately represent macro-scale material properties, which are influenced by structural imperfections and compositional variations in real-world samples, thus limiting their practical applicability. To address these challenges, we propose DARWIN 1.5, the largest open-source large language model tailored for materials science. By leveraging natural language as input, DARWIN eliminates the need for task-specific descriptors and enables a flexible, unified approach to material property prediction and discovery. Our approach integrates 6M material domain papers and 21 experimental datasets from 49,256 materials across modalities while enabling cross-task knowledge transfer. The enhanced model achieves up to 59.1% improvement in prediction accuracy over the base LLaMA-7B architecture and outperforms SOTA machine learning approaches across 8 materials design tasks. These results establish LLMs as a promising foundation for developing versatile and scalable models in materials science.