Prediction and optimization of epoxy adhesive strength from a small dataset through active learning.

ABSTRACT: 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 combining laboratory experimental design, an active learning pipeline and Bayesian optimization. We start from an initial dataset of 32 adhesive samples that were prepared from various molecular-weight bisphenol A-based epoxy resins and polyetheramine curing agents, mixing ratios and curing temperatures, and our da

SUBMITTER: Pruksawan S 

PROVIDER: S-EPMC6818118 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature