The process of discovering and developing new materials currently requires considerable effort, time, and expense. Machine learning (ML) algorithms can potentially provide quick and accurate methods for screening new materials. In the present work, the features of the metal organic frameworks (MOFs) as a catalyst for fixing carbon dioxide into cyclic carbonate were extracted to build a data set, which were collected from the experimental results of approximately 100 published papers. Classifiers were trained with the data set with various ML algorithms, including support vector machine (SVM), K-nearest neighbor classification (KNN), decision trees (DT), stochastic gradient descent (SGD), and neural networks (NN), to predict the catalytic performance. The ML models were trained on 80% of the data set and then tested on the remaining 20% to predict the carbon dioxide fixation ability. The trained ML model was extended to explore 1311 hypothetical MOFs, and some structures displayed a strong catalytic ability. Finally, the six best metal ions (Mn, V, Cu, Ni, Zr and Y) and four best ligands (tactmb, tdcbpp, TCPP, H₃L) were determined. These six metals and four ligands could be combined into 24 MOFs, which are strongly potential catalysts for carbon dioxide fixation. Using machine learning methods can speed up the screening of materials, and this methodology is promising for application not only to MOFs as catalysts but also in many other materials science projects.

目前，发现和开发新材料的过程需要相当多的努力、时间和费用。机器学习 (ML) 算法有可能为筛选新材料提供快速准确的方法。在目前的工作中，提取了金属有机骨架 (MOF) 作为将二氧化碳固定成环状碳酸酯的催化剂的特征，以建立一个数据集，该数据集是从大约 100 篇已发表论文的实验结果中收集的。分类器使用各种 ML 算法的数据集进行训练，包括支持向量机 (SVM)、K-最近邻分类 (KNN)、决策树 (DT)、随机梯度下降 (SGD) 和神经网络 (NN)，以预测催化性能。ML 模型在 80% 的数据集上进行训练，然后在剩余的 20% 上进行测试以预测二氧化碳固定能力。将训练好的 ML 模型扩展到探索 1311 个假设的 MOF，一些结构表现出很强的催化能力。最后，六种最佳金属离子（Mn、V、Cu、Ni、Zr 和 Y）和四种最佳配体（tactmb、tdcbpp、TCPP、H₃ L) 被确定。这六种金属和四种配体可以组合成 24 个 MOF，它们是固定二氧化碳的潜在催化剂。使用机器学习方法可以加快材料的筛选，这种方法不仅有望应用于作为催化剂的 MOF，还可以应用于许多其他材料科学项目。