Standardizing benchmarking methods for ML optimization algorithms will be crucial as the number of optimization methodologies increases. Foundational work has been laid by Lapkin research group with the release of the Summit open-source software package . Given the vast spectrum of chemical reactions, there is a necessity to develop a diverse array of reaction models to comprehensively assess the suitability of optimization methods for various scenarios. The field should leverage the ability of high throughput experimentation to produce large amounts of data to create reliable forward models that can be incorporated into an online repository. Thus, researchers could access this online repository to benchmark new optimization algorithms by performing in-silico optimization campaigns of the chemical reaction models.