I implement most algorithms I develop into Julia packages. These codes are slowly developing into a useful set of molecular simulation packages with Python ase bindings via ASE.jl. Once packages become "useful", I normally register then in the domain-specific JuliaMolSim registry:
JuliaMolSim; see also the landing page. There is now a small Julia Chemistry community developing.
ACE.jl : Julia implementation of the Atomic Cluster Expansion; utilities and extensions provided by the ACEsuit software suite.
JuLIP.jl : base library for interatomic potentials and molecular simulation, mostly materials oriented, modelled on ase.
ASE.jl : bi-directional JuLIP and ase connectivity
SaddleSearch.jl : experimental library for saddle point search algorithms, including walker and string methods
SlaterKoster.jl : implements the Slater-Koster transformation as the basis of tight-binding models
NBodyIPs.jl : interatomic potentials for materials based on the PIP formalism
JuLIPMaterials.jl : code collection for atomistic material simulation, e.g., elasticity, boundary conditions, ...
Isaac.jl : Experimental Newton-Krylov methods for minimisation and saddle point search
TightBinding.jl : an experimental tight-binding code with some unique features, in particular it supports site energies for QM/MM multi-scale methods
See my github page for more.
I collected some Julia example/tutorial notebooks that I wrote to teach myself Julia. They are intended primarily as a rapid introduction to Julia for numerical analysts, mostly from the point of view of differential equations, and most useful for somebody familiar with Matlab. For more details see the github repository.