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

https://mdr.nims.go.jp/datasets/3531cb90-075f-4b92-9922-17ba4ba6d95e

## File

- [Prediction_and_optimization_of_epoxy_adhesive_strength_from_a_small_dataset_through_active_learning.pdf](https://mdr.nims.go.jp/filesets/cb79e4f4-bdb7-4180-8ba8-b4c69d29cd60/download) ([Detail](https://mdr.nims.go.jp/filesets/cb79e4f4-bdb7-4180-8ba8-b4c69d29cd60.md))

## Id

3531cb90-075f-4b92-9922-17ba4ba6d95e

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2021-08-05T16:24:11.377778Z

## Updated at

2024-01-05T13:11:50.004655Z

## Published at

2021-08-13T18:54:56.833967Z

## Doi



## First published url

https://doi.org/10.1080/14686996.2019.1673670

## Date published

2019-12-31

## Recorded date published

2019-12-31

## Resource type

journal_article

## Manuscript type

authors_original

## Collection



## Title

- title: Prediction and optimization of epoxy adhesive strength from a small dataset
    through active learning
  title_type: original
  lang: en

## Description

- description: 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 algo- rithms 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 prepara- tions 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.
  description_type: abstract
  lang: en

## Creator

- name: Lambard, Guillaume
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079
- name: Naito, Masanobu
  role: author
  orcid: https://orcid.org/0000-0001-7198-819X
- name: Samitsu, Sadaki
  role: author
  orcid: https://orcid.org/0000-0002-4139-1656
- name: Pruksawan, Sirawit
  role: author
  orcid: https://orcid.org/0000-0002-9380-1872
- name: Sodeyama, Keitaro
  role: author
  orcid: https://orcid.org/0000-0002-9228-0729

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: active learning
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: adhesive
  schema: not_defined

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## Specimen



## Chemical composition



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## Fileset

- id: cb79e4f4-bdb7-4180-8ba8-b4c69d29cd60
  filename: Prediction_and_optimization_of_epoxy_adhesive_strength_from_a_small_dataset_through_active_learning.pdf
  content_type: application/pdf
  size: 1897146
  md5: 88e4d6266145472eba1ce8d298c9417e

## Thumbnail

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filename: Prediction_and_optimization_of_epoxy_adhesive_strength_from_a_small_dataset_through_active_learning.pdf