Article Dans Une Revue Journal of the American Chemical Society Année : 2024

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

Données de formation informatives pour une prédiction efficace des propriétés dans les structures métallo-organiques par apprentissage actif

Résumé

In recent data-driven approaches to materials discovery, scenarios where target quantities are expensive to compute or 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 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 dataset 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 helps revealing patterns in the data, thereby offering a unique tool to efficiently analyze complex structure-property relationships in materials and accelerate materials discovery.

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Dates et versions

hal-04789719 , version 1 (18-11-2024)

Identifiants

Citer

Ashna Jose, Emilie Devijver, Noel Jakse, Roberta Poloni. Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning. Journal of the American Chemical Society, 2024, ⟨10.1021/jacs.3c13687⟩. ⟨hal-04789719⟩
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