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Emerging tools bring forth fresh approaches to work, and the field of naturalscience is no different. In natural science, traditional manual, serial, andlabour-intensive work is being augmented by automated, parallel, and iterativeprocesses driven by artificial intelligence-based experimental automation andmore. To add new capabilities in natural science, enabling the acceleration andenrichment of automation of the discovery process, we present DARWIN, a seriesof tailored LLMs for natural science, mainly in physics, chemistry, andmaterial science. This series relies on open-source LLM, incorporatingstructured and unstructured scientific knowledge from public datasets andliterature. We fine-tuned the models using over 60,000 instruction data points,emphasizing factual correctness. During the fine-tuning, we introduce theScientific Instruction Generation (SIG) model, automating instructiongeneration from scientific texts. This eliminates the need for manualextraction or domain-specific knowledge graphs and efficiently injectsscientific knowledge into the model. We also explore multi-task trainingstrategies, revealing interconnections between scientific tasks. DARWIN seriesnot only achieves state-of-the-art results on various scientific tasks but alsodiminishes reliance on closed-source AI models. Our research showcases theability of LLM in the scientific domain, with the overarching goal of fosteringprosperity within the broader AI for science community.

DARWIN Series: Domain Specific Large Language Models for Natural Science