Domain‐Trained Language Model for Inverse Design and Synthesis of High‐Performance Hydrogen Storage MOFs
Abstract
Abstract A domain‐specific large language model, MOFs‐LLM, is developed to accelerate the inverse design and synthesis of metal—organic frameworks (MOFs) for hydrogen storage. Trained on 210 million tokens derived from over 6 000 MOF‐related publications and 15 000 crystal structures, the model integrates chemical knowledge with structural features to improve structure–property reasoning. Compared to baseline methods, MOFs‐LLM achieves a 46.7% enhancement in capturing structure–property relationships. It enables the inverse design of 60 candidate frameworks optimized for both hydrogen storage performance and synthetic accessibility. Guided by the model, a novel MOF (Cu‐LLMs‐1) was synthesized in three experimental iterations, exhibiting a hydrogen uptake of 1.33 wt% at room temperature, ranking among the top five pure MOFs under comparable conditions. These findings highlight the potential of domain‐trained language models to bridge virtual screening and experimental realization in materials discovery.
Article Details
Authors (14)
Zhimeng Liu
Yuqiao Su
Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 P.R. China
Hao Wang
Division of Quantitative Sciences, Department of Oncology Johns Hopkins University School of Medicine Baltimore Maryland USA
Tao Ban
State Key Laboratory of Fluorine & Nitrogen Chemicals School of Chemical Engineering and Technology Xi'an Jiaotong University Xi'an 710049 China
Lingmeng Wang
Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 P.R. China
Shaopeng Lu
Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 P.R. China
Zuoshuai Xi
Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 P.R. China
Wenqing Li
Yujie Guo
CAS Key Laboratory of Molecular Nanostructure and Nanotechnology and Beijing National Laboratory for Molecular Sciences
Changan Wang
Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 P.R. China
Xiaoqi Wang
School of History
Xu Jin
Hongyi Gao
Department of Pathology, Guangdong Women and Children Hospital
Ge Wang