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Prediction and optimization of epoxy adhesive strength from a small dataset through active learning
Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples) limits the development of chemical intuition from experimentalists, as it constrains the use of machine-learning algorithms for extracting relevant information. We tackle this issue to predict and optimize adhesive materials by combiningdoi:10.6084/m9.figshare.10007918.v1 fatcat:oli6peauqzbivlh2uvdcbkqkgu