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

doi:10.1016/S1872-2067(25)64725-5

Abstract

Abstract:

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.

Key words: Large language model, Chemical engineering, Process development, Multidimensional benchmark, Domain adaptation

Jibin Zhou, Feiyang Xu, Zhijun Chang, Duiping Liu, Lulu Li, Jian Cui, Yi Li, Xin Li, Li Qian, Zhixiong Zhang, Guoping Hu, Mao Ye, Zhongmin Liu. From lab to fab: A large language model for chemical engineering[J]. Chinese Journal of Catalysis, 2025, 73: 159-173.

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URL: https://www.cjcatal.com/EN/10.1016/S1872-2067(25)64725-5

https://www.cjcatal.com/EN/Y2025/V73/I6/159

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Data source	Document	Size
Scholarly paper	1.06 million	30.5 GB
Chemical patent	5.79 million	58.9 GB
Professional book	1200	106.2 GB

Data source	Document	Size
Scholarly paper	1.06 million	30.5 GB
Chemical patent	5.79 million	58.9 GB
Professional book	1200	106.2 GB

Type	Catalyst	Simulation	Equipment	Separation	Safety	Heat	Engineering
Multiple choice	24600	43900	48600	96700	9200	10800	8000
True/False	14100	46800	42400	80100	5800	10000	7000
Fill-in-the-blank	19500	39900	44100	83100	9000	10000	2000
Calculation	30500	54200	63700	116700	13100	12500	5000
Short answer	31500	46300	63000	117500	13000	11200	7000
Sum	120200	231100	261800	494100	50100	54500	29000
Sum	1240800

Type	Catalyst	Simulation	Equipment	Separation	Safety	Heat	Engineering
Multiple choice	24600	43900	48600	96700	9200	10800	8000
True/False	14100	46800	42400	80100	5800	10000	7000
Fill-in-the-blank	19500	39900	44100	83100	9000	10000	2000
Calculation	30500	54200	63700	116700	13100	12500	5000
Short answer	31500	46300	63000	117500	13000	11200	7000
Sum	120200	231100	261800	494100	50100	54500	29000
Sum	1240800

Dimension	Definition	Score range
Objectivity	the question should have a unique and objective answer under unified evaluation standards	0-5
Rationality	the question and answer must be complete and clear, without omitting critical information	0-5
Accuracy	the reasoning chain should be checked step by step to ensure the absence of factual, logical, computational, or knowledge errors	0-5
Generalizability	questions and answers should be based on general domain knowledge rather than relying on specific papers or patents	0-5