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NU-CUCIS / ElemNet

Deep Learning the Chemistry of Materials From Only Elemental Composition for Enhancing Materials Property Prediction

Why this job: works in the areas we cover, but does not say which job it does

What it does

ElemNet is a deep neural network model that takes only the elemental compositions as inputs and leverages artificial intelligence to automatically capture the essential chemistry to predict materials properties. ElemNet can automatically learn the chemical interactions and similarities between different elements which allows it to even predict the phase diagrams of chemical systems absent from the training dataset more accurately than the conventional machine learning models based on physical attributes levaraging domain knowledge.

Get started

No single install command. Read the steps in the README (opens in new tab)

Maintenance
Inactive
Last commit 10 months ago
Stars
104
On GitHub
Licence
Custom
Read before business use
Downloads for
None listed
No release files
Latest version
None yet
No releases
Built with
Jupyter Notebook
98% of the code

Is it looked after?

Contributors
Not stated
Last 90 days
Issues
67% closed
2 still open
Pull requests
16 merged
0 waiting
Releases
None
Never published
  • No contributing guide
  • No security policy
  • No code of conduct

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More: languages, topics and project record

Languages

  • Jupyter Notebook 98%
  • Python 2%
  • Scala <1%

Topics

  • chemistry
  • deep-learning
  • deep-neural-networks
  • machine-learning
  • materials-science

Project record

  • JobOther AI tools
  • StatusListed
  • Commits83
  • Forks35
  • Watching5
  • Stars in 28 days0
  • CreatedOct 9, 2018
  • First seen by usNov 7, 2026
  • CheckedNov 8, 2026

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