diff --git a/README.md b/README.md
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 - Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in August 2024
 - 352 total pages or 326 body pages
 - 109 figures
+- [Final Version PDF](https://github.com/amkrajewski/PhD-Dissertation/releases/download/Final/PhD_Dissertation_Submitted.pdf)
 
 ## Abstract
 
@@ -33,6 +34,10 @@ The resulting multi-level discovery infrastructure is highly generalizable as it
 - Appendix Chapter F - **MatSE580 Guest Lecture 2 - Running ML Models in pySIPFENN and Guiding Limited DFT Calculations Based on KS2022 Embedding Cluster Exploration**
 - Appendix Chapter G - **nimCSO Basic Tutorial on Selecting Elements for High Entropy Alloy Modeling**
 
+## Graphical Abstract
+
+![graphicalabstract](https://github.com/amkrajewski/PhD-Dissertation/blob/e009b6c4cb6be0ecaef12207f1a690908ddd5f01/intro/DissertationBigPicture.png)
+
 ## Vita 
 
 Adam Krajewski was born in Europe, where he spent his childhood and received pre-college education at a school nationally recognized for its university-level chemistry curriculum. He first came to the United States in 2013 and moved completely in 2015 to join the Materials Science Department at Case Western Reserve University. Within the first two months, Adam began research in Prof. Welsch's group. After just one year, he enrolled in graduate courses and also joined Prof. Willard's group, progressively moving from experiments towards theory, modeling, and simulations. In the Fall of 2017, he enrolled in graduate courses in Artificial Intelligence, starting to specialize in applying AI techniques, including Machine Learning, to his research which became focused hidden process modeling, materials data processing, and data-driven design of magnetocaloric metallic glasses.