With the recent advances in artificial intelligence (AI) and machine learning (ML), the discovery of novel materials has been democratized in such a drastic way, that everyone with a computer can potentially simulate novel chemical compounds to help solve some of humanities grand challenges such as compounds for solar energy conversion, disease prevention or new materials for both efficient pollutant degradation and energy storage. The literature is full of theoretical studies and predictions however worryingly few contain actual synthesis and experimental validation of these materials due to the complications that are typically involved with synthesizing such compounds. To overcome the lack of actual synthesized compounds, this project attempts to answer the following question: Is it possible to use data-driven approaches to incorporate synthesis protocols directly into simulation-based compound discovery? Answering this question will pave the way for a new and improved discovery process of molecules and compounds that potentially are easy and more realistic to make and validate. Additionally, this it will pave the way to automate the process from simulation to product – similarly to how the automobile production industry was revolutionized more than 100 years ago by Henry Ford.