# Bayesian optimization, an active learning method for optimising experimental parameters

https://mdr.nims.go.jp/datasets/259ef794-7f84-4cfe-a861-beaf4b341266

## File

- [seminar_BO_Junier.pdf](https://mdr.nims.go.jp/filesets/700e95c3-8c3c-4e01-82a1-83c71ff8d0d6/download) ([Detail](https://mdr.nims.go.jp/filesets/700e95c3-8c3c-4e01-82a1-83c71ff8d0d6.md))

## Id

259ef794-7f84-4cfe-a861-beaf4b341266

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-11-21T05:16:18.446854Z

## Updated at

2024-12-23T03:30:48.312818Z

## Published at

2025-01-20T02:31:27.690648Z

## Doi

https://doi.org/10.48505/nims.5198

## First published url

https://www.icmpe.cnrs.fr/agenda/seminaire-icmpe-de-sebastien-junier/

## Date published



## Recorded date published



## Resource type

conference_presentation

## Manuscript type

na

## Collection



## Title

- title: Bayesian optimization, an active learning method for optimising experimental
    parameters
  title_type: original
  lang: en

## Description

- description: "A supervised machine learning model (SML) is a method of artificial
    intelligence (AI) that enables\r\nthe estimation of the value of various properties
    (called ”target” values) from the values of other\r\nproperties (called ”features”).
    SML is a particularly valuable tool in situations where it is not possible to
    calculate the target values using an analytical function, and more over in cases
    where the target values are challenging or time-consuming to determine. Such an
    approach can be applied to values obtained through numerical calculation or experimental
    measurement. In general, SML requires a big amount of data, typically ranging
    from hundreds to several thousand data points. Applying this method to experimental
    synthesis can be difficult, as the synthesis of more than a hundred compounds
    may prove impossible. Bayesian optimization (BO) represents a specific type of
    SML within a narrower category of models known as active learning. The objective
    is to identify an optimal value for the target properties with a minimum amount
    of data. To achieve this, we start with a modest amount of data and incrementally
    increase it, selecting the most promising potential values of features through
    a combination of Gaussian process and SML. The key strength of BO lies in its
    acquisition function, which guides the selection of the next point to evaluate.
    This function intelligently trades off between exploring uncertain regions and
    exploiting promising areas, allowing BO to converge to the optimum with less data
    than to other optimization methods. In this seminar, I will present the fundamental
    concepts of BO and demonstrate its practical application using MADGUI, a fully
    graphical interface developed by C. Bajan and G. Lambard at NIMS. This tool makes
    BO accessible to researchers without extensive programming experience."
  description_type: abstract
  lang: eng

## Creator

- name: LAMBARD Guillaume
  role: author
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Materials Design Group
  ror: https://ror.org/026v1ze26
- name: BAJAN Christophe Marie Olivier
  role: author
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Materials Design Group
  ror: https://ror.org/026v1ze26

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## Keyword

- subject: Bayesian optimization
  schema: not_defined
- subject: Active learning
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

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## Data origin

- data_origin_type: other

## Embargo



## Journal



## Conference

name: Bayesian optimization, an active learning method for optimising experimental
  parameters
start_date: 2024-11-07
end_date: 2024-11-07

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## Fileset

- id: 700e95c3-8c3c-4e01-82a1-83c71ff8d0d6
  filename: seminar_BO_Junier.pdf
  content_type: application/pdf
  size: 4202900
  md5: 88bbff66d8ff45146ca99d1b78f54a4b

## Thumbnail

fileset_id: 700e95c3-8c3c-4e01-82a1-83c71ff8d0d6
filename: seminar_BO_Junier.pdf