From 71f0d03e47362ce688687a818555a04ad2e85297 Mon Sep 17 00:00:00 2001
From: Ruocheng Han <ruocheng.han@chem.uzh.ch>
Date: Wed, 2 Jun 2021 08:48:05 +0000
Subject: [PATCH] Update README.md

---
 README.md | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/README.md b/README.md
index 4de7291..bda617a 100644
--- a/README.md
+++ b/README.md
@@ -241,6 +241,37 @@ do
 done
 ```
 
+## References
+For the use of this code (EnergyDensity) in your research, please consider citing following papers:
+1. Han, R.; Rodríguez-Mayorga, M.; Luber, S. A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds. J. Chem. Theory Comput. 2021, 17, 777. 
+2. Han, R.; Luber, S. Fast Estimation of Møller–Plesset Correlation Energies Based on Atomic Contributions. J. Phys. Chem. Lett. 2021, 12, 5324–5331.
 
+```bash
+@article{mp2_ed,
+  doi = {10.1021/acs.jctc.0c00898},
+  url = {https://doi.org/10.1021/acs.jctc.0c00898},
+  year = {2021},
+  month = jan,
+  publisher = {American Chemical Society ({ACS})},
+  volume = {17},
+  number = {2},
+  pages = {777--790},
+  author = {Ruocheng Han and Mauricio Rodr{\'{\i}}guez-Mayorga and Sandra Luber},
+  title = {A Machine Learning Approach for {MP}2 Correlation Energies and Its Application to Organic Compounds},
+  journal = "J. Chem. Theory Comput."
+}
+
+@article{mpn_ed,
+  doi = {10.1021/acs.jpclett.1c00900},
+  url = {https://doi.org/10.1021/acs.jpclett.1c00900},
+  year = {2021},
+  month = jun,
+  publisher = {American Chemical Society ({ACS})},
+  pages = {5324--5331},
+  author = {R. Han and S. Luber},
+  title = {Fast Estimation of M{\o}ller{\textendash}Plesset Correlation Energies Based on Atomic Contributions},
+  journal = "J. Phys. Chem. Lett."
+}
 
+```
 
-- 
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