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mace
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.ACE.jl
Parameterisation of Equivariant Properties of Particle Systemsmace-jax
Equivariant machine learning interatomic potentials in JAX.ACEpotentials.jl
Machine Learning Interatomic Potentials with the Atomic Cluster Expansionmace-mp
MACE-MP modelsmace-layer
Higher order equivariant graph neural networks for 3D point cloudsACE1.jl
Atomic Cluster Expansion for Modelling Invariant Atomic Propertiesmace-off
MACE-OFF23 modelsObjectPools.jl
thread-safe and flexible temporary arrays and object cache for semi-manual memory managementACEhamiltonians.jl
Polynomials4ML.jl
Polynomials for ML: fast evaluation, batching, differentiationEquivariantModels.jl
Tools for geometric learningACEHAL
ACEfit.jl
Generic Codes for Fitting ACE modelsACEhamiltoniansExamples
Example code for the ACEhamiltonians code-base.IPFitting.jl
Fitting of NBodyIPsACEds.jl
Coarse-grained dynamical systemsACEmd.jl
UltraFastACE.jl
Experimenting with faster ACE potentialsACE1x.jl
Experimental features for ACE1.jlACE1docs.jl
User Documentation for ACEsuitACEpsi.jl
ACE wave function parameterizationsBIPs.jl
Boost-Invariant Polynomials for jet taggingACEsktb.jl
experimental code for tight-binding hamiltoniansACEatoms.jl
Generic code for modelling atomic properties using ACEDecoratedParticles.jl
GeomOpt.jl
Geometry optimization interfaceHyperActiveLearning.jl
An accelerate dynamics stragety for collecting training data.ACEcalculator.jl
Evaluate ACE interatomic potentials and interfacesACEdocs_old
Documentaton for ACEsuitWithAlloc.jl
A simple Bumper convenience extensionACEluxpots.jl
ACE potentials via EquivariantModels and LuxRepLieGroups.jl
Representations of Lie Groupsace.cpp
Experimental C++ routines for the ACE basisLove Open Source and this site? Check out how you can help us