Minor improvements

Author: mfherbst

docs/src/features.md

@@ -32,7 +32,8 @@ and runs out of the box on Linux, windows and macOS, see [Installation](@ref).
 ## Response and response properties
 - Density-functional perturbation theory (DFPT)
 - Integration of DFPT with **algorithmic differentiation**,
-  e.g. [Polarizability using automatic differentiation](@ref)
+  e.g. [Elastic constants](@ref),
+  [Polarizability using automatic differentiation](@ref)
 - [Phonon computations](@ref) *(preliminary implementation)*
 
 ## Unique features

docs/src/guide/introductory_resources.md

@@ -67,21 +67,22 @@ see [Publications](@ref).
   Covers topics such as DFT, pseudos, SCF, response, ...
 
 ## Recordings
+- [Algorithmic differentiation (AD) for plane-wave DFT](https://www.youtube.com/watch?v=g6j1beYSWV4) by M. F. Herbst:
+  45-min talk at the Institute for Pure and Applied Mathematics (IPAM)
+  at UCLA discussing the algorithmic differentiation techniques in DFTK.
+
 - [DFTK.jl: 5 years of a multidisciplinary electronic-structure code](https://www.youtube.com/watch?v=ox_j2zKOuIk) by M. F. Herbst:
   30-min talk at JuliaCon 2024 providing the state of DFTK 5 years after the project was started.
   [Slides](https://michael-herbst.com/talks/2024.07.12_5years_DFTK.pdf),
   [Pluto notebook](https://michael-herbst.com/talks/2024.07.12_5years_DFTK.html)
 
-- [Julia for Materials Modelling](https://www.youtube.com/watch?v=dujepKxxxkg) by M. F. Herbst:
+- [Julia for Materials Modelling](https://www.youtube.com/watch?v=dujepKxxxkg)
+  by M. F. Herbst (from 2023):
   One-hour talk providing an overview of materials modelling tools for Julia.
   Key features of DFTK are highlighted as part of the talk.
+  Starts to become a little outdated
   [Pluto notebooks](https://mfherbst.github.io/julia-for-materials/)
 
-- [DFTK: A Julian approach for simulating electrons in solids](https://www.youtube.com/watch?v=-RomkxjlIcQ) by M. F. Herbst:
-  Pre-recorded talk for JuliaCon 2020.
-  Assumes no knowledge about DFT and gives the broad picture of DFTK. Starts to become a little outdated.
-  [Slides](https://michael-herbst.com/talks/2020.07.29_juliacon_dftk.pdf).
-
 - [Juliacon 2021 DFT workshop](https://www.youtube.com/watch?v=HvpPMWVm8aw) by M. F. Herbst:
   Three-hour workshop session at the 2021 Juliacon providing a mathematical look on
   DFT, SCF solution algorithms as well as the integration of DFTK into the Julia

src/response/chi0.jl

@@ -575,6 +575,7 @@ function construct_bandtol(Bandtol::Type, basis::PlaneWaveBasis, ψ, occupation:
     # Distributing the error equally across all k-points leads to (with w = sqrt(Ω / Ng))
     #   ‖z_{nk}‖ ≤ sqrt(Ω / Ng) / (‖K v_i‖ sqrt(Nocck) ‖Re(F⁻¹ Φk)‖_{2,∞} * 2f_{nk} Nk wk)
     # If we bound ‖Re(F⁻¹ Φk)‖_{2,∞} from below this is sqrt(Nocc / Ω).
+    # See also section SM6 and Table SM4 in 2505.02319.
     #
     # Note that the kernel term ||K v_i|| of 2505.02319 is dropped here as it purely arises
     # from the rescaling of the RHS performed in apply_χ0 above. Consequently the function
@@ -598,10 +599,13 @@ function construct_bandtol(Bandtol::Type, scfres::NamedTuple; kwargs...)
 end
 
 function adaptive_bandtol_orbital_term_(::Type{BandtolGuaranteed}, basis, kpt, ψk, mask_k)
-    # Orbital term ‖Re(F⁻¹ Φk)‖_{2,∞} for thik k-point
+    # Orbital term ‖F⁻¹ Φk‖_{2,∞} for thik k-point
+    # Note that compared to the paper we deliberately do not take the real part,
+    # since taking the real part represents an additional approximation
+    # (thus making the strategy less guaranteed)
     row_sums_squared = sum(mask_k) do n
         ψnk_real = @views ifft(basis, kpt, ψk[:, n])
-        abs2.(real.(ψnk_real))
+        abs2.(ψnk_real)
     end
     sqrt(maximum(row_sums_squared))
 end

src/scf/anderson.jl

@@ -103,6 +103,8 @@ and returns ``xₙ₊₁``.
     M = stack(Pfxs) .- vec(Pfxₙ)  # Mᵢⱼ = (Pfxⱼ)ᵢ - (Pfxₙ)ᵢ
     # We need to solve 0 = M' Pfxₙ + M'M βs <=> βs = - (M'M)⁻¹ M' Pfxₙ
 
+    # TODO If memory pressure is high, automatically drop old iterates here
+    #      (Note: This quickly happens in GPU scenarios)
     # Ensure the condition number of M stays below maxcond, else prune the history
     Mfac = qr(M)
     while size(M, 2) > 1 && cond(Mfac.R) > anderson.maxcond

src/terms/operators.jl

@@ -153,7 +153,7 @@ function apply!(Hψ, op::DivAgradOperator, ψ;
         Hψ.fourier .-= im .* G_plus_k[α] .* A∇ψ ./ 2
     end
 end
-# TODO Implement  Matrix(op::DivAgrad)
+# TODO Implement  Matrix(op::DivAgradOperator)
 
 
 # Optimize RFOs by combining terms that can be combined