From lab to fab: A large language model for chemical engineering

The development of chemical technologies, which involves a multistage process covering laboratory research, scale-up to industrial deployment, and necessitates interdisciplinary collaboration, is often accompanied by substantial time and economic costs. To address these challenges, in this work, we report ChemELLM, a domain-specific large language model (LLM) with 70 billion parameters for chemical engineering. ChemELLM demonstrates state-of-the-art performance across critical tasks ranging from foundational understanding to professional problem-solving. It outperforms mainstream LLMs (e.g., O1-Preview, GPT-4o, and DeepSeek-R1) on ChemEBench, the first multidimensional benchmark for chemical engineering, which encompasses 15 dimensions across 101 distinct essential tasks. To support robust model development, we curated ChemEData, a purpose-built dataset containing 19 billion tokens for pre-training and 1 billion tokens for fine-tuning. This work establishes a new paradigm for artificial intelligence-driven innovation, bridging the gap between laboratory‐scale innovation and industrial‐scale implementation, thus accelerating technological advancement in chemical engineering. ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.