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Machine Learning Interatomic Potentials with the Atomic Cluster Expansion

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ACEpotentials.jl facilitates the creation and use of atomic cluster expansion (ACE) interatomic potentials. See the documentation for installation instructions, tutorials, and more.

Notes on Versions

  • Version 0.6.x uses ACE1.jl as a backend. It is mature and suitable for linear models with few species. This is not longer actively developed, but critical bugfixes can still be provided. [docs-v0.6]
  • Version 0.7.x is reserved
  • Version 0.8.x and onwards provides a new and much more flexible implementation, and integrates with the AtomsBase ecosystem. Most but not all features from 0.6.x have been ported to this re-implementation. Usability should be the same or improved for most end-users. For developers this provides a much more flexible framework for experimentation. [docs-v0.8]

Contributing

Contributions are very welcome. Until clear guidelines and practices are established, we recommend to open an issue where the bugfix or enhancement can be discussed, before starting a pull request. We will do our best to respond in a timely manner.

Quick Start

  • Install Julia 1.10
  • Create new folder a.g. acetutorial; Open a shell
  • Create a new project in acetutorial and install ACEpotentials.jl
julia --project=. 
] 
registry add https://github.com/ACEsuit/ACEregistry
add ACEpotentials
  • Install the Julia tutorials (this installs two Jupyter notebook tutorials)
using ACEpotentials
ACEpotentials.copy_tutorial()
  • Work through the tutorials.

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Machine Learning Interatomic Potentials with the Atomic Cluster Expansion

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  • Julia 80.4%
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