# [Research Highlights Vol.45] Artificial Intelligence Learns to Predict Photo-Functional Molecules

https://mdr.nims.go.jp/datasets/425b8a45-2e53-463b-9ef0-075ce62e1cdb

## File

- [[Vol. 45]Artificial Intelligence Learns to Predict Photo-Functional Molecules_ WPI-MANA.pdf](https://mdr.nims.go.jp/filesets/31e2fcdc-2638-44ee-aaab-ff7e7bd9b559/download) ([Detail](https://mdr.nims.go.jp/filesets/31e2fcdc-2638-44ee-aaab-ff7e7bd9b559.md))

## Id

425b8a45-2e53-463b-9ef0-075ce62e1cdb

## Local identifier

identifier: research-highlights/00045

## Visibility

open_to_public

## State

published

## Created at

2022-06-25T13:59:23.004899Z

## Updated at

2023-12-24T15:30:22.692714Z

## Published at

2022-12-16T05:02:54.547413Z

## Doi

https://doi.org/10.48505/nims.3785

## First published url

https://www.nims.go.jp/mana/research/highlights/vol45.html

## Date published

2018-12-20

## Recorded date published

2018-12-20

## Resource type

magazine

## Manuscript type

vor

## Collection

- id: e1c8d7be-5bbc-4156-96cf-4c0efce6b473
  identifier: https://mdr.nims.go.jp/pid/e1c8d7be-5bbc-4156-96cf-4c0efce6b473
  title: Research Highlights

## Title

- title: "[Research Highlights Vol.45] Artificial Intelligence Learns to Predict Photo-Functional
    Molecules"
  title_type: original
  lang: en

## Description

- description: Artificial intelligence can be used to design new molecules; it is
    becoming a popular tool because of its potential for discovering molecules in
    unexplored chemical spaces, its ability to screen a huge number of potential molecules
    in a short amount of time and its tendency to find unconventional ways of solving
    problems. However, whether such molecules can be actually synthesized and whether
    they display the desired functionalities in the real world is an open question.
  description_type: abstract
  lang: en

## Creator

- name: International Center for Materials Nanoarchitectonics (WPI-MANA)
  role: author
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: National Institute for Materials Science
ror: https://ror.org/026v1ze26

## Managing organization



## Keyword

- subject: artificial intelligence
  schema: not_defined
- subject: organic molecules
  schema: not_defined
- subject: molecule design
  schema: not_defined
- subject: density functional theory
  schema: not_defined
- subject: photofunctional molecules
  schema: not_defined

## Rights

- description: In Copyright
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: MANA E-BULLETIN
  volume: '45'

## Conference



## Related item

- identifier: https://doi.org/10.1021/acscentsci.8b00213
  identifier_type: DOI
  relation_type: refers
  related_item_type: dataset

## 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: 31e2fcdc-2638-44ee-aaab-ff7e7bd9b559
  filename: "[Vol. 45]Artificial Intelligence Learns to Predict Photo-Functional Molecules_
    WPI-MANA.pdf"
  content_type: application/pdf
  size: 136395
  md5: e9424da6f4bc730604c2bd1d5761081b

## Thumbnail

fileset_id: 31e2fcdc-2638-44ee-aaab-ff7e7bd9b559
filename: "[Vol. 45]Artificial Intelligence Learns to Predict Photo-Functional Molecules_
  WPI-MANA.pdf"