From 44f01c21e5089a0d7bd85c2d109660f5c29dbf01 Mon Sep 17 00:00:00 2001
From: Sean Kavanagh <s.kavanagh19@imperial.ac.uk>
Date: Fri, 12 Jul 2024 11:44:20 -0400
Subject: [PATCH] Fill out citation docs page

---
 docs/cite.rst | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/docs/cite.rst b/docs/cite.rst
index 9f8296cc..8cb1c107 100644
--- a/docs/cite.rst
+++ b/docs/cite.rst
@@ -1,3 +1,25 @@
-Citing Nequip
+Citing NequIP
 =============
+If you use ``NequIP`` in your research, please cite our `article <https://doi.org/10.1038/s41467-022-29939-5>`_:
 
+.. code-block::
+
+    @article{batzner_e3-equivariant_2022,
+      title = {E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials},
+      author = {Batzner, Simon and Musaelian, Albert and Sun, Lixin and Geiger, Mario and Mailoa, Jonathan P. and Kornbluth, Mordechai and Molinari, Nicola and Smidt, Tess E. and Kozinsky, Boris},
+      year = {2022},
+      month = may,
+      journal = {Nature Communications},
+      volume = {13},
+      number = {1},
+      pages = {2453},
+      issn = {2041-1723},
+      doi = {10.1038/s41467-022-29939-5},
+    }
+
+The theory behind NequIP is described in our `article <https://doi.org/10.1038/s41467-022-29939-5>`_ above.
+NequIP's backend builds on `e3nn <https://e3nn.org>`_, a general framework for building E(3)-equivariant
+neural networks (1). If you use this repository in your work, please consider citing ``NequIP`` and ``e3nn`` (2):
+
+ 1. https://e3nn.org
+ 2. https://doi.org/10.5281/zenodo.3724963