# Ranking Pareto optimal solutions based on projection free energy

https://mdr.nims.go.jp/datasets/43c56784-98e6-4715-9d51-4d7654d28892

## File

- [PhysRevMaterials.7.093804.pdf](https://mdr.nims.go.jp/filesets/6a68233e-73e1-4775-bf44-fd2c6e450dcd/download) ([Detail](https://mdr.nims.go.jp/filesets/6a68233e-73e1-4775-bf44-fd2c6e450dcd.md))

## Id

43c56784-98e6-4715-9d51-4d7654d28892

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-09-29T20:17:03.635103Z

## Updated at

2024-01-05T13:11:58.470593Z

## Published at

2023-10-04T04:30:09.530727Z

## Doi



## First published url

https://doi.org/10.1103/PhysRevMaterials.7.093804

## Date published

2023-09-19

## Recorded date published

2023-9

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Ranking Pareto optimal solutions based on projection free energy
  title_type: original
  lang: en

## Description

- description: Based on available datasets prepared by numerical simulations and machine
    learning, maps of properties for materials that have not yet been synthesized
    can be developed. These maps can be used to select promising materials for synthetic
    experiments. With a single objective function, the ranking of the optimal solutions
    can be simply obtained based on the values of the target property. However, applications
    with multiple target properties require the calculation of Pareto optimal solutions
    to visualize trade-offs. These solutions are generally ranked manually, selecting
    the weight of the multiple objectives based on prior knowledge. In this study,
    to provide an automated ranking of Pareto solutions, we introduced the most-isolated
    Pareto solution (MIPS) score, which is defined by a projection free energy. Using
    the MIPS ranking, it is possible to appropriately select the most isolated materials
    predicted in the property space. To verify the effectiveness of the proposed method,
    we used a database of semiconductors created by density-functional theory. Our
    method was able to correctly select and rank the most isolated solutions in both
    convex and concave two- dimensional Pareto frontiers, outperforming the most relevant
    outlier detection methods. We also demonstrated that our approach can be easily
    extended to three-dimensional property spaces.
  description_type: abstract
  lang: eng

## Creator

- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Kei Terayama
  role: author
- name: Masato Sumita
  role: author
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: American Physical Society (APS)

## Managing organization



## Keyword

- subject: Pareto solutions
  schema: not_defined
- subject: free energy
  schema: not_defined
- subject: semiconductor
  schema: not_defined

## Rights

- identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Physical Review Materials
  issn: '24759953'
  volume: '7'
  issue: '9'
  article_number: '93804'

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## Fileset

- id: 6a68233e-73e1-4775-bf44-fd2c6e450dcd
  filename: PhysRevMaterials.7.093804.pdf
  content_type: application/pdf
  size: 996390
  md5: 734432ca329138414b132300fd6a3ec7

## Thumbnail

fileset_id: 6a68233e-73e1-4775-bf44-fd2c6e450dcd
filename: PhysRevMaterials.7.093804.pdf