Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/avik-pal/atomicgraphnets.jl

Atomic graph models for molecules and crystals in Julia

https://github.com/avik-pal/atomicgraphnets.jl

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Atomic graph models for molecules and crystals in Julia

Basic Info
  • Host: GitHub
  • Owner: avik-pal
  • License: mit
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 9.18 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Fork of Chemellia/AtomicGraphNets.jl
Created over 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme Contributing License

README.md

AtomicGraphNets.jl

Run tests codecov Dev

AtomicGraphNets.jl implements graph-based models for machine learning on atomic systems, such as Crystal Graph Convolutional Neural Nets, in Julia. It makes use of the Flux ecosystem for model building and the JuliaGraphs ecosystem for graph representation and visualization, as well as adapting some features from GeometricFlux.

Documentation is in progress over here.

Getting Started

  1. To install the latest tagged version, in your Julia REPL, do ]add AtomicGraphNets. However, you can also play with the latest version on the main branch by skipping to step 2 and then doing ]add /path/to/repo where you replace the dummy path with the location of your clone.

  2. Clone this package to wherever you want to play.

  3. Go and try out the example in examples/example1/ – it has its own README file with detailed instructions.

Contributing

We welcome community contributions! Please refer to contribution guide for suggestions on how to go about things.

Acknowledgements

Many thanks to Dhairya Gandhi for helping out with some adjoints to actually make these layers trainable! :D

Owner

  • Name: Avik Pal
  • Login: avik-pal
  • Kind: user
  • Location: Cambridge, MA
  • Company: Massachusetts Institute of Technology

PhD Student @mit || Prev: BTech CSE IITK

GitHub Events

Total
Last Year