cesmix-mit/PotentialLearning.jl

PotentialLearning.jl: Optimize your atomistic data and interatomic potential models in your molecular dynamics workflows.

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This tool helps computational material scientists and chemists efficiently develop accurate interatomic potential models for molecular dynamics simulations. It takes atomistic datasets, often generated by expensive Density Functional Theory (DFT) calculations, and intelligently subsamples them to reduce computational cost. The output is a refined dataset and optimized interatomic potential models, ready for use in simulations.

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Use this if you need to reduce the number of expensive Density Functional Theory (DFT) calculations required to train your interatomic potential models while maintaining accuracy, or if you want to optimize the hyperparameters of your models.

Not ideal if you are not working with atomistic simulations or do not need to optimize interatomic potential models for molecular dynamics.

molecular-dynamics computational-materials-science atomistic-simulations quantum-chemistry materials-modeling
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 17 / 25

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23

Forks

10

Language

Julia

License

MIT

Last pushed

Nov 21, 2024

Commits (30d)

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