Informative Training Data for Efficient Property Prediction in Metal–Organic Frameworks by Active Learning

Abstract

In recent data-driven approaches to material discovery, scenarios where target quantities are expensive to compute and measure are often overlooked. In such cases, it becomes imperative to construct a training set that includes the most diverse, representative, and informative samples. Here, a novel regression tree-based active learning algorithm is employed for such a purpose. It is applied to predict the band gap and adsorption properties of metal−organic frameworks (MOFs), a novel class of materials that results from the virtually infinite combinations of their building units. Simpler and low dimensional descriptors, such as those based on stoichiometric and geometric properties, are used to compute the feature space for this model owing to their ability to better represent MOFs in the low data regime. The partitions given by a regression tree constructed on the labeled part of the data set are used to select new samples to be added to the training set, thereby limiting its size while maximizing the prediction quality. Tests on the QMOF, hMOF, and dMOF data sets reveal that our method constructs small training data sets to learn regression models that predict the target properties more efficiently than existing active learning approaches, and with lower variance. Specifically, our active learning approach is highly beneficial when labels are unevenly distributed in the descriptor space and when the label distribution is imbalanced, which is often the case for real world data. The regions defined by the tree help in revealing patterns in the data, thereby offering a unique tool to efficiently analyze complex structure−property relationships in materials and accelerate materials discovery.

Efficient Xylene Isomer Separation: Accelerated Screening with Active Learning and Molecular Simulation

“…Active learning (AL) is a promising approach for improving efficiency of the ML model by dynamically acquiring valuable training data. − Although sequential AL screening methods have been proposed, they can be time-consuming when dealing with large database . A more efficient strategy would be to jointly acquire multiple data points (here, hMOFs) in each AL iteration to evaluate the potential candidates in batches.…”

Optimizing the Prediction of Adsorption in Metal-Organic Frameworks Leveraging Q-Learning

“…In the realm of ML, various methods have been utilized to screen MOFs and discern their adsorption behavior. These methods encompass support vector machines (SVMs) 17,18 , neural networks [19][20][21][22][23] , random forests 24,25 , among others. However, this paper directs its focus towards one particularly potent approach-reinforcement learning (RL).…”