# A general-purpose organic gel computer that learns by itself

https://mdr.nims.go.jp/datasets/16f280ee-aa69-4fcb-b651-db93af85a27b

## File

- [Sahoo_2023_Neuromorph._Comput._Eng._3_044007.pdf](https://mdr.nims.go.jp/filesets/d401ece9-a994-40e3-b82e-0a621238dcee/download) ([Detail](https://mdr.nims.go.jp/filesets/d401ece9-a994-40e3-b82e-0a621238dcee.md))

## Id

16f280ee-aa69-4fcb-b651-db93af85a27b

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-09-02T01:16:53.886522Z

## Updated at

2024-11-13T07:30:37.946739Z

## Published at

2024-11-13T07:30:38.044825Z

## Doi



## First published url

https://doi.org/10.1088/2634-4386/ad0fec

## Date published

2023-12-01

## Recorded date published

2023-12-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: A general-purpose organic gel computer that learns by itself
  title_type: original
  lang: en

## Description

- description: To build energy minimized superstructures, self-assembling molecules
    explore astronomical options, colliding ∼109 molecules s−1. Thus far, no computer
    has used it fully to optimize choices and execute advanced computational theories
    only by synthesizing supramolecules. To realize it, first, we remotely re-wrote
    the problem in a language that supramolecular synthesis comprehends. Then, all-chemical
    neural network synthesizes one helical nanowire for one periodic event. These
    nanowires self-assemble into gel fibers mapping intricate relations between periodic
    events in any-data-type, the output is read instantly from optical hologram. Problem-wise,
    self-assembling layers or neural network depth is optimized to chemically simulate
    theories discovering invariants for learning. Subsequently, synthesis alone solves
    classification, feature learning problems instantly with single shot training.
    Reusable gel begins general-purpose computing that would chemically invent suitable
    models for problem-specific unsupervised learning. Irrespective of complexity,
    keeping fixed computing time and power, gel promises a toxic-hardware-free world.
  description_type: abstract
  lang: und

## Creator

- name: Pathik Sahoo
  role: author
  orcid: https://orcid.org/0000-0002-5102-9482
  organization: National Institute for Materials Science
- name: Pushpendra Singh
  role: author
  orcid: https://orcid.org/0000-0002-7274-6683
  organization: National Institute for Materials Science
- name: Komal Saxena
  role: author
  organization: National Institute for Materials Science
- name: Subrata Ghosh
  role: author
  organization: National Institute for Materials Science
- name: R P Singh
  role: author
- name: Ryad Benosman
  role: author
- name: Jonathan P Hill
  role: author
  orcid: https://orcid.org/0000-0002-4229-5842
  organization: National Institute for Materials Science
- name: Tomonobu Nakayama
  role: author
  orcid: https://orcid.org/0000-0001-9696-475X
  organization: National Institute for Materials Science
- name: Anirban Bandyopadhyay
  role: author
  orcid: https://orcid.org/0000-0002-8823-4914
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: IOP Publishing

## Managing organization



## Keyword

- subject: " organic computer, Optical vortex, Non-algorithmic computing, helical
    nanowire, Single shot learning, Deep learning network, Clique problem"
  schema: not_defined

## Rights

- description: "Original content from this work may be used under the terms of the
    Creative Commons Attribution 4.0 licence. Any further distribution of this work
    must\r\nmaintain attribution to the author(s) and the title of the work, journal
    citation and DOI.\r\n"
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Neuromorphic Computing and Engineering
  issn: '26344386'
  volume: '3'
  issue: '4'
  article_number: '044007'

## 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: d401ece9-a994-40e3-b82e-0a621238dcee
  filename: Sahoo_2023_Neuromorph._Comput._Eng._3_044007.pdf
  content_type: application/pdf
  size: 1944200
  md5: 83f736d6b868d2d4b91b07415de4221c

## Thumbnail

fileset_id: d401ece9-a994-40e3-b82e-0a621238dcee
filename: Sahoo_2023_Neuromorph._Comput._Eng._3_044007.pdf