add workflow

.github/workflows/draft-pdf.yml

@@ -0,0 +1,23 @@
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/qmctorch.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper.pdf

paper/qmctorch.md

@@ -40,6 +40,9 @@ to facilitate its utilisation and help its adoption by quantum chemists and mate
 `QMCTorch` is a Python package using PyTorch [paszke2019pytorch] as a backend to perform Quantum Monte-Carlo (QMC) simulations of molecular systems. Many software such as `QMCPack`[@qmcpack], `QMC=Chem` [@qmcchem], `CHAMP` [@champ] provide high-quality implementation of advanced QMC methodologies in low-level languages (C++/Fortran).  Python implementations of QMC such as `PAUXY` [@pauxy] and `PyQMC` [@pyqmc] have also been proposed to facilitate the use and development of QMC techniques. Recently large efforts have been made to leverage recent development of deep learning techniques for QMC simulations. Hence neural-network based wave-function ansatz has been proposed [@paulinet,@ferminet]. These recent advances lead to very interesting results but lack in explainability of the resulting wave function ansatz. `QMCTorch` allows to perform QMC simulations using physically motivated neural netwrok architecture that closely follows the wnave function ansatz used by QMC practitioners. As such, it still allows to leverage automatic differentiation for the calculation of the gradients of the total energy wrt the variational parameters and the GPU capabilites offered by PyTorch without loosing the physical intuition behind the wave function ansatz. 
 
 
+# Architecture
+![General architectureof the neural network used by `QMCTorch` to encodie the wave function ansatz.\label{fig:arch}](qmctorch2.png)
+
 # Mathematics
 
 Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$