# Machine Learning-Based Experimental Design in Materials Science

https://mdr.nims.go.jp/datasets/bdfcceb0-aeb3-4d80-835a-bc91df27172f

## File

- [Dieb-Tsuda2018_Chapter_MachineLearning-BasedExperimen.pdf](https://mdr.nims.go.jp/filesets/eaea4853-5181-4189-a1f2-d6056cec78a1/download) ([Detail](https://mdr.nims.go.jp/filesets/eaea4853-5181-4189-a1f2-d6056cec78a1.md))

## Id

bdfcceb0-aeb3-4d80-835a-bc91df27172f

## Local identifier

identifier: mdr-schema-yaml/1r66j202q

## Visibility

open_to_public

## State

published

## Created at

2021-08-05T16:24:08.101029Z

## Updated at

2022-10-02T16:58:06.506765Z

## Published at

2021-08-13T18:55:36.953369Z

## Doi



## First published url

https://doi.org/10.1007/978-981-10-7617-6_4

## Date published

2018-01-16

## Recorded date published

2018

## Resource type

book

## Manuscript type

na

## Collection



## Title

- title: Machine Learning-Based Experimental Design in Materials Science
  title_type: original
  lang: en

## Description

- description: "In materials design and discovery processes, optimal experimental
    design (OED) algorithms are getting more popular. OED is often modeled as an optimization
    of a black-box function. In this chapter, we introduce two machine learning-based
    approaches for OED: Bayesian optimization (BO) and Monte Carlo tree search (MCTS).
    BO is based on a relatively complex machine learning model and has been proven
    effective in a number of materials design problems. MCTS is a simpler and more
    efficient approach that showed significant success in the computer Go game. We
    discuss existing OED applications in materials science and discuss future directions.\r\n"
  description_type: abstract
  lang: en

## Creator

- name: Tsuda, Koji
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606
- name: Dieb, Thaer M.
  role: author
  orcid: https://orcid.org/0000-0002-8111-2009

## Contact agent



## Publisher

organization: Springer, Singapore

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## Keyword

- subject: Materials design
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: Optimal experiment design
  schema: not_defined

## Rights

- description: Creative Commons BY Attribution 4.0 International
  identifier: https://creativecommons.org/licenses/by/4.0/

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## Fileset

- id: eaea4853-5181-4189-a1f2-d6056cec78a1
  filename: Dieb-Tsuda2018_Chapter_MachineLearning-BasedExperimen.pdf
  content_type: application/pdf
  size: 465032
  md5: 9f96a91ded45be4e8f6d9c90dafa16ff

## Thumbnail

fileset_id: eaea4853-5181-4189-a1f2-d6056cec78a1
filename: Dieb-Tsuda2018_Chapter_MachineLearning-BasedExperimen.pdf