The number of registered packages in the General registry is quite vast (about 2800 on 11/12/2018) and growing every day. So if you have a reasonably standard problem you want to solve, there is a good chance that someone has already made an effort.
To give you some ideas about the community and what is available, here is a non-exhaustive list, which is naturally influenced by my own background. I'm happy to adapt it, if you feel something is wrong / missing.
Without further ado:
import Pkg; Pkg.add("TensorOperations") using TensorOperations N = 3 I = randn(N, N, N, N); C = randn(N, N); @tensor It[i,j,k,l] := I[α,β,γ,δ] * C[α, i] * C[β, j] * C[γ, k] * C[δ, l]
import Pkg; Pkg.add("Interact") using Interact using Plots Vmorse(r; re=0.7, α=1.3, D=10) = D*(1 - exp(-α * (r - re)))^2 - D r = collect(0:0.1:10) mp = @manipulate for α in slider(0:0.1:4; label="Steepness α"), D in slider(0:0.4:30, label="depth D"), re in slider(0:0.1:4, label="Equilibrium re") p = plot(r, Vmorse.(r, re=re, α=α, D=D), label="Vmorse re=$re α=$α D=$D", ylim=(-30, 30)) end
Plenty of standard file formats in scientific computing and elsewhere can be used in Julia:
Not too many projects in chemistry and materials science have adopted Julia so far. Here are a few:
The optimisation community has adopted Julia rather early and the respective Julia libraries are at a very good shape.
The driving force behind the Julia optimisation community is the JuliaOpt organisation with its working horse JuMP. This package defines a metalanguage for optimisation problems, which can be combined with about 20 open-source or commercial optimisation solvers. A pretty exhaustive set of problem classes are supported: Linear programming, (mixed) integer programming, semidefinite programming, nonlinear programming, ...
For certain highly specific use cases a number of specialised packages have emerged: