Metal–organic framework clustering through the lens of transfer learning
Metal–organic framework clustering through the lens of transfer learning
复制标题
通过迁移学习的视角进行金属有机框架聚类
DOI：
10.1039/d3me00016h
复制
发表时间：
2023
影响因子：
3.6
通讯作者：
Colón, Yamil J.
中科院分区：
文献类型：
--
作者：
Cooper, Gregory M.;Colón, Yamil J.
金属有机框架（MOFs）是具有各种应用的有前途的材料，机器学习（ML）技术可以使其设计和理解结构-性能关系。在本文中，我们使用机器学习（ML）使用两种不同的方法对MOF进行聚类。对于第一组聚类，我们使用纹理属性对数据进行分解，并对得到的组件进行聚类。我们分别集群的MOF空间的拓扑结构。然后，来自每个集群的特征数据被输入到单独的神经网络（NN）中，用于直接学习吸附任务（甲烷或氢气）。然后将得到的NN用于迁移学习（TL），其中仅重新训练最后一个NN层。结果表明，TL性能的显着差异的基础上，集群选择直接学习。我们发现TL性能取决于直接和TL中涉及的聚类之间在分解的特征空间中的欧氏距离。当TL同时在两种类型的簇和吸附任务中进行时，发现了类似的结果。我们注意到，甲烷吸附是一个更好的源任务比氢吸附。总的来说，该方法能够识别具有最多可转移信息的MOF，从而获得有价值的见解和对MOF景观的更全面的理解。这突出了该方法的潜力，以产生更深入的了解复杂的系统，并提供了一个机会，其应用在替代数据集。
Metal–organic frameworks (MOFs) are promising materials with various applications, and machine learning (ML) techniques can enable their design and understanding of structure–property relationships. In this paper, we use machine learning (ML) to cluster the MOFs using two different approaches. For the first set of clusters, we decompose the data using the textural properties and cluster the resulting components. We separately cluster the MOF space with respect to their topology. The feature data from each of the clusters were then fed into separate neural networks (NNs) for direct learning on an adsorption task (methane or hydrogen). The resulting NNs were then used in transfer learning (TL) where only the last NN layer was retrained. The results show significant differences in TL performance based on which cluster is chosen for direct learning. We find TL performance depends on the Euclidean distance in the decomposed feature space between the clusters involved in the direct and TL. Similar results were found when TL was performed simultaneously across both types of clusters and adsorption tasks. We note that methane adsorption was a better source task than hydrogen adsorption. Overall, the approach was able to identify MOFs with the most transferable information, leading to valuable insights and a more comprehensive understanding of the MOF landscape. This highlights the method's potential to generate a deeper understanding of complex systems and provides an opportunity for its application in alternative datasets.