ArticleScience and technology of advanced materials2019
Prediction and optimization of epoxy adhesive strength from a small dataset through active learning.
Article in Science and technology of advanced materials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Active learning sequential surrogate-based optimization (AL-SSBO) versus response surface methodology in sunflower oil diacylglycerol synthesis: Performance and insight into AL-SSBO.Food chemistry: X · 2026Article
- An informed regression-based knowledge distillation framework for simultaneous prediction of physical and mechanical properties of thermoset epoxy polymers.Scientific reports · 2026Article
- Bridging small molecule calculations and predictable polymer mechanical properties.Nature communications · 2025Article
- Accelerated Development of Novel Biomass-Based Polyurethane Adhesives via Machine Learning.ACS applied materials & interfaces · 2025Article
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Efficient Compressive Strength Prediction of Alkali-Activated Waste Materials Using Machine Learning.Materials (Basel, Switzerland) · 2024Article
- Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning.Materials (Basel, Switzerland) · 2024Article
- Integrated hybrid modeling and SHAP (SHapley Additive exPlanations) to predict and explain the adsorption properties of thermoplastic polyurethane (TPU) porous materials.RSC advances · 2024Article
- Data quantity governance for machine learning in materials science.National science review · 2023Article
- Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations.Nanomaterials (Basel, Switzerland) · 2022Article
- Effect of Epoxy Structure on Properties of Waterborne Coatings and Electrical Steel Laminates.Polymers · 2022Article
- Topological alternation from structurally adaptable to mechanically stable crosslinked polymer.Science and technology of advanced materials · 2022Article
- Machine-Learning-Guided Discovery ofJournal of the American Chemical Society · 2021Article
- Prediction of Lap Shear Strength and Impact Peel Strength of Epoxy Adhesive by Machine Learning Approach.Nanomaterials (Basel, Switzerland) · 2021Article
- Home-based transcranial direct current stimulation (tDCS) and motor imagery for phantom limb pain using statistical learning to predict treatment response: an open-label study protocol.Principles and practice of clinical research (2015) · 2021Article
- Environmentally friendly recycling system for epoxy resin with dynamic covalent bonding.Science and technology of advanced materials · 2021Article
- Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning.Science and technology of advanced materials · 2020Article
- Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach.Science and technology of advanced materials · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 data-driven method allows us to propose an optimal preparation of an adhesive material with a very high adhesive joint strength measured at 35.8 ± 1.1 MPa after three active learning cycles (five proposed preparations per cycle). A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient of determination, root mean square error and mean absolute error of 0.85, 4.0 MPa and 3.0 MPa, respectively. This study demonstrates the important impact of active learning to accelerate the design and development of tailored highly functional materials from very small datasets.
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