From 094e3646f65bc341cb2fdef79b3d8018b3a715e1 Mon Sep 17 00:00:00 2001
From: zhaoli2023 <43864970+Zhaoli2042@users.noreply.github.com>
Date: Mon, 28 Jun 2021 20:10:04 -0500
Subject: [PATCH] Update README.md

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5afda4e..d7dcc11 100644
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+++ b/README.md
@@ -12,7 +12,7 @@ This work was supported by the U.S. Department of Energy, Office of Basic Energy
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 **Citation**<br/>
 **1.** Bucior, B. J.; Bobbitt, N. S.; Islamoglu, T.; Goswami, S.; Gopalan, A.; Yildirim, T.; Farha, O. K.; Bagheri, N.; Snurr, R. Q. Energy-Based Descriptors to Rapidly Predict Hydrogen Storage in Metal–Organic Frameworks. Mol. Syst. Des. Eng. 2019, 4 (1), 162–174. <br/>
-**2.** Li, Z.; Bucior, B. J.; Chen H., Haranczyk, M.; Siepmann, J. I.; Snurr, R. Q. 	Machine Learning Using Host/Guest Energy Histograms to Predict Adsorption in Metal-Organic Frameworks: Application to Short Alkanes and Xe/Kr Mixtures. J. Chem. Phys. <em>In Press<em>.
+**2.** Li, Z.; Bucior, B. J.; Chen H., Haranczyk, M.; Siepmann, J. I.; Snurr, R. Q. 	Machine Learning Using Host/Guest Energy Histograms to Predict Adsorption in Metal-Organic Frameworks: Application to Short Alkanes and Xe/Kr Mixtures. J. Chem. Phys. <em>In Press</em>.
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 **Usage**<br/>
 For detailed usages of this code, please go to the **R/** source code directory. <br/>