准确率达97%，普林斯顿大学等提出MOFSeq-LMM，高效预测MOFs能否被合成

Core Insights - A joint research team from Princeton University and the Colorado School of Mines has developed a machine learning-based method for efficiently predicting the free energy of Metal-Organic Frameworks (MOFs), significantly reducing computational costs and enabling high-throughput thermodynamic assessments [2][12]. Group 1: Research Methodology - The proposed method utilizes a large language model (LLM) to predict free energy directly from the structural sequences of MOFs, achieving an F1 score of 97% in determining whether the free energy exceeds a threshold for synthetic feasibility [2][29]. - The research team constructed a large dataset named MOFMinE, which includes approximately 1 million MOF prototypes, providing comprehensive information from component selection to functional modifications [7][10]. - The MOFSeq-LMM model framework was developed to facilitate efficient free energy predictions, transforming MOF structural information into a computer-readable sequence representation [12][13]. Group 2: Data Characteristics - MOFMinE encompasses 1,393 topological templates, 27 inorganic building blocks, 14 organic building blocks, and 19 basic edge building blocks, ensuring diversity in chemical and topological structures [10]. - A subset of 65,574 structures within MOFMinE contains free energy data, which is utilized for fine-tuning and testing the LLM [11]. Group 3: Model Performance - The LLM-Prop model, designed for material property predictions, achieved an average absolute error of 0.789 kJ/mol per MOF atom in free energy predictions, with a high correlation coefficient (R² = 0.990) [21]. - The model demonstrated a successful rate of approximately 78% in identifying the most stable polymorphs among 7,490 polymorphic families, indicating its potential for high-throughput screening [30][32]. Group 4: Implications for the Industry - The integration of AI in MOFs research is reshaping the methodologies and innovation pace within materials science, moving from traditional experimental approaches to data-driven predictions [34][36]. - The development of structured knowledge graphs like MOF-ChemUnity aims to standardize naming conventions and enhance the accessibility of MOF-related data, further facilitating research and development in this field [35].