diff --git a/.gitignore b/.gitignore
index 90354d9..2215d66 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
+# VSCode
+.vscode/
+
 # HPC scripts and logs
 hpc/
 
diff --git a/Hamiltonian.jl b/Hamiltonian.jl
index a206677..4e18862 100644
--- a/Hamiltonian.jl
+++ b/Hamiltonian.jl
@@ -68,8 +68,8 @@ end
 
 "cuTENSOR contraction and accumulation (C = A * B + C)"
 function contract_accumulate!(C::CuTensor, A::CuTensor, B::CuTensor)::CuTensor
-    CUTENSOR.contraction!(one(eltype(C)), A.data, A.inds, CUTENSOR.CUTENSOR_OP_IDENTITY, B.data, B.inds, CUTENSOR.CUTENSOR_OP_IDENTITY,
-                          one(eltype(C)), C.data, C.inds, CUTENSOR.CUTENSOR_OP_IDENTITY, CUTENSOR.CUTENSOR_OP_IDENTITY)
+    cuTENSOR.contraction!(one(eltype(C)), A.data, A.inds, cuTENSOR.CUTENSOR_OP_IDENTITY, B.data, B.inds, cuTENSOR.CUTENSOR_OP_IDENTITY,
+                          one(eltype(C)), C.data, C.inds, cuTENSOR.CUTENSOR_OP_IDENTITY, cuTENSOR.CUTENSOR_OP_IDENTITY)
     return C
 end
 
@@ -125,6 +125,7 @@ function (H::Hamiltonian)(v)
 end
 
 tolerance = 1e-6
+max_iterations = 500
 
 "Wrapper for KrylovKit.eigsolve"
 function eig(H::Hamiltonian{T}, levels::Int; resonances = !H.hermitian)::Tuple{Vector,Vector,KrylovKit.ConvergenceInfo} where {T<:Float}
@@ -134,7 +135,7 @@ function eig(H::Hamiltonian{T}, levels::Int; resonances = !H.hermitian)::Tuple{V
         x₀ = CUDA.rand(Complex{T}, vectorDims(H)...)
         synchronize()
     end
-    evals, evecs, info = eigsolve(H, x₀, levels, resonances ? :LI : :SR; ishermitian = H.hermitian, tol = tolerance, krylovdim = levels * 8)
+    evals, evecs, info = eigsolve(H, x₀, levels, resonances ? :LI : :SR; ishermitian = H.hermitian, tol = tolerance, krylovdim = levels * 8, maxiter = max_iterations)
     info.converged < levels && throw(error("Not enough convergence"))
     if H.hermitian evals = real.(evals) end
     if H.mode == gpu_cutensor # to avoid possible GPU memory leak
diff --git a/LocalPreferences.toml b/LocalPreferences.toml
new file mode 100644
index 0000000..cdb275e
--- /dev/null
+++ b/LocalPreferences.toml
@@ -0,0 +1,2 @@
+[TensorOperations]
+precompile_workload = true
diff --git a/Project.toml b/Project.toml
new file mode 100644
index 0000000..1ef80cd
--- /dev/null
+++ b/Project.toml
@@ -0,0 +1,7 @@
+[deps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
+NVTX = "5da4648a-3479-48b8-97b9-01cb529c0a1f"
+Preferences = "21216c6a-2e73-6563-6e65-726566657250"
+TensorOperations = "6aa20fa7-93e2-5fca-9bc0-fbd0db3c71a2"
+cuTENSOR = "011b41b2-24ef-40a8-b3eb-fa098493e9e1"
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..57a0500
--- /dev/null
+++ b/README.md
@@ -0,0 +1,27 @@
+# DVR.jl
+
+Solves the quantum $n$-body problem in finite volume (lattice) with periodic boundary conditions. Uses discrete variable representation (DVR) with optional support for complex scaling to study resonances. All details can be found in [H. Yu, N. Yapa, and S. König, Complex scaling in finite volume, Phys. Rev. C 109, 014316 (2024)](https://doi.org/10.1103/PhysRevC.109.014316).
+
+Written in Julia with optional CUDA GPU acceleration (experimental).
+
+## Installation
+
+Make sure you have Julia installed. Required packages can be installed with a single command:
+```bash
+julia --project=. -e 'import Pkg; Pkg.instantiate()'
+```
+
+## Usage
+
+See `calculations/3b_bound.jl` for an example on a 3-body bound state.
+See `calculations/3b_res_from_paper.jl` for an example of a 3-body resonance via complex scaling.
+
+## Planned features
+
+- [ ] Spin and isospin degrees of freedom for nuclear calculations
+- [ ] Multi-node HPC support
+- [ ] Parity and cubic symmetries ($S_4$)
+
+## Acknowledgments
+
+The author gratefully acknowledges the guidance from Sebastian König.
diff --git a/calculations/EC-R2R-S.jl b/calculations/EC-R2R-S.jl
new file mode 100644
index 0000000..9e9351a
--- /dev/null
+++ b/calculations/EC-R2R-S.jl
@@ -0,0 +1,67 @@
+using Plots, Arpack
+
+include("../helper.jl")
+include("../Hamiltonian.jl")
+
+mode = cpu_tensor
+T = Float32 # single-precision mode
+
+V_r2(c) = r2 -> c * (-5 * exp(-r2/3) + 2 * exp(-r2/10))
+
+d = 3
+n = 2
+N = 48
+L = 30
+ϕ = pi/6
+n_imag = 1
+s = system{T}(d, n, N, L)
+
+train_cs = range(0.78, 0.45, length=5)
+train_ref = reverse([0.05387926313545913-0.008900278182520881im,
+    0.11254295298924327-0.020515067379548786im,
+    0.16060154707503538-0.03716539208626717im,
+    0.19741353362674618-0.05994519982799412im,
+    0.2219100763497223-0.08959449893439568im])
+
+extrapolate_cs = range(0.38, 0.22, length=5)
+extrapolate_ref = reverse([0.23165109150003316-0.12052751440975719im,
+    0.23190549514995962-0.1406687118589838im,
+    0.22763660218046278-0.1626190970863793im,
+    0.21807104244164865-0.18635600686249373im,
+    0.2020979906072586-0.21180157628258728im])
+
+training_E = ComplexF64[]
+training_vec = Array[]
+exact_E = ComplexF64[]
+extrapolated_E = ComplexF64[]
+
+for c in train_cs
+    println("Training c=", c)
+    H = Hamiltonian{T}(s, V_r2(c), ϕ, n_imag, mode)
+    @time evals, evecs, info = eig(H, 20, resonances = true)
+    i = nearestIndex(evals, pop!(train_ref))
+    push!(training_E, evals[i])
+    push!(training_vec, evecs[i])
+end
+
+N_EC = [sum(x .* y) for (x, y) in Iterators.product(training_vec, training_vec)]
+
+for c in extrapolate_cs
+    println("Extrapolating c=", c)
+    H = Hamiltonian{T}(s, V_r2(c), ϕ, n_imag, mode)
+    @time evals, _, info = eig(H, 40, resonances = true)
+    nearestE = nearest(evals, pop!(extrapolate_ref))
+    push!(exact_E, nearestE)
+    
+    # EC extrapolation
+    H_training_vec = H.(training_vec)
+    H_EC = [sum(x .* y) for (x, y) in Iterators.product(training_vec, H_training_vec)]
+
+    evals = eigvals(H_EC, N_EC)
+    push!(extrapolated_E, nearestE)
+end
+
+scatter(real.(training_E), imag.(training_E), label="training")
+scatter!(real.(exact_E), imag.(exact_E), label="exact")
+scatter!(real.(extrapolated_E), imag.(extrapolated_E), label="extrapolated")
+savefig("temp/EC-R2R-S.pdf")
diff --git a/helper.jl b/helper.jl
new file mode 100644
index 0000000..ee9da11
--- /dev/null
+++ b/helper.jl
@@ -0,0 +1,5 @@
+"Index of the nearest value in a list to a given reference point"
+nearestIndex(list::Array, ref) = argmin(norm.(list .- ref))
+
+"Nearest value in a list to a given reference point"
+nearest(list::Array, ref) = list[nearestIndex(list, ref)]