# Extraction of physicochemical laws by symbolic regression using a Bayesian information criterion

https://mdr.nims.go.jp/datasets/067cdfe3-e317-4183-a9b5-d5e4db81533b

## File

- [Extraction of physicochemical laws by symbolic regression using a Bayesian information criterion.pdf](https://mdr.nims.go.jp/filesets/c61b2bec-e87d-49d5-a306-a883b320390b/download) ([Detail](https://mdr.nims.go.jp/filesets/c61b2bec-e87d-49d5-a306-a883b320390b.md))

## Id

067cdfe3-e317-4183-a9b5-d5e4db81533b

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-11-13T02:58:23.031459Z

## Updated at

2024-11-14T07:30:30.027796Z

## Published at

2024-11-14T07:30:30.123673Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2024.2420658

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Extraction of physicochemical laws by symbolic regression using a Bayesian
    information criterion
  title_type: original
  lang: en

## Description

- description: ベイズ情報基準を活用した独自のSymbolic regressionアルゴリズム
  description_type: abstract
  lang: eng

## Creator

- name: Naoki Yamane
  role: author
  organization: Tsukuba University
- name: Kan Hatakeyama-Sato
  role: author
  organization: 東京工業大学
- name: Yuma Iwasaki
  role: author
  orcid: https://orcid.org/0000-0002-7117-277X
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Materials Design Group
- name: Yasuhiko Igarashi
  role: author
  organization: Tsukuba University

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Machine learning
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '4'
  issue: '1'

## Conference



## Related item



## Funding



## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: c61b2bec-e87d-49d5-a306-a883b320390b
  filename: Extraction of physicochemical laws by symbolic regression using a Bayesian
    information criterion.pdf
  content_type: application/pdf
  size: 2039913
  md5: 2eaeaaa627682207285398359b12a8d2

## Thumbnail

fileset_id: c61b2bec-e87d-49d5-a306-a883b320390b
filename: Extraction of physicochemical laws by symbolic regression using a Bayesian
  information criterion.pdf