MOFEXPO：基于三千种金属有机框架的提取稳定性数据的机器学习模型
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks
摘要：
我们报告了一个工作流程和输出的自然语言处理（NLP）为基础的程序来挖掘现存的金属有机框架（MOF）的文献描述结构特征的MOF及其溶剂去除和热稳定性。我们从文本挖掘中获得了2，000多个溶剂去除稳定性指标，从热重分析数据中获得了3，000个热分解温度。我们通过与手工标记的子集进行比较来评估我们的NLP方法的有效性和我们提取的数据的准确性。使用基于图形和孔隙几何结构的表示在此数据上训练的机器学习（ML，即人工神经网络）模型能够预测具有量化不确定性的新MOF的稳定性。我们的Web界面MOFestival为用户提供了对我们精选数据的访问权限，并使他们能够利用这些数据对新的MOFs进行预测。MOF还鼓励社区对现有数据和ML模型预测的反馈，以基于社区的主动学习改进MOF稳定性模型。
英文摘要：
We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
