# 2024 Nobel prizes in physics and chemistry: from neural network models to materials engineering

https://mdr.nims.go.jp/datasets/47f0d137-3332-477a-9e31-6f0428b85892

## File

- [2024 Nobel prizes in physics and chemistry  from neural network models to materials engineering.pdf](https://mdr.nims.go.jp/filesets/8c46cdda-f715-4176-a363-62135ccb7b88/download) ([Detail](https://mdr.nims.go.jp/filesets/8c46cdda-f715-4176-a363-62135ccb7b88.md))

## Id

47f0d137-3332-477a-9e31-6f0428b85892

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-07-16T04:33:55.858157Z

## Updated at

2025-07-16T23:30:16.024969Z

## Published at

2025-07-16T23:20:14.722669Z

## Doi

https://doi.org/10.48505/nims.5599

## First published url

https://doi.org/10.1080/27660400.2025.2516307

## Date published

2025-12-31

## Recorded date published

2025-12-31

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: '2024 Nobel prizes in physics and chemistry: from neural network models to
    materials engineering'
  title_type: original
  lang: en

## Description

- description: In this review, I will discuss the reasons for the 2024 Nobel Prize
    in Physics, the second neural network boom and its demise, which cannot be ignored,
    and the third neural network boom, backed by steady academic progress. In addition,
    I will discuss AI for Science, advocated by Demis Hassabis, winner of the Nobel
    Prize in Chemistry. The contributions of Japanese researchers whose work cannot
    be ignored will be described, and a new perspective will be presented on information
    creation, statistical mechanics, and data-driven science. AI for materials engineering,
    which is an extension of AI for science, is explained in terms of the 3 + 1 model
    of functional expression and the three levels of data-driven science proposed
    by our group.
  description_type: abstract
  lang: en

## Creator

- name: Masato Okada
  role: author
  organization: The University of Tokyo
  department: Department of Complexity Science and Engineering, Graduate of Frontier
    Sciences

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Neural network
  schema: not_defined
- subject: Neuroscience
  schema: not_defined
- subject: AI
  schema: not_defined
- subject: AI for science
  schema: not_defined
- subject: Data-driven science
  schema: not_defined
- subject: AI for materials engineering
  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
  issn: '27660400'
  volume: '5'
  article_number: '2516307'

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## Specimen



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## Fileset

- id: 8c46cdda-f715-4176-a363-62135ccb7b88
  filename: 2024 Nobel prizes in physics and chemistry  from neural network models
    to materials engineering.pdf
  content_type: application/pdf
  size: 1531921
  md5: 061a03572c4c59d0e473ec7c1c9af396

## Thumbnail

fileset_id: 8c46cdda-f715-4176-a363-62135ccb7b88
filename: 2024 Nobel prizes in physics and chemistry  from neural network models to
  materials engineering.pdf