Toward Machine Learning-Enhanced High-Throughput Experimentation

Recent literature suggests that the fields of machine learning (ML) and high throughput experimentation (HTE) have separately received considerable attention from chemists and engineers, leading to the development of powerful reactivity models and platforms capable of rapidly performing thousands of reactions. The merger of ML with HTE presents a wealth of opportunities for the exploration of chemical space, but the integration of the two has yet to be fully realized. We highlight examples of recent developments in ML and HTE that collectively suggest the utility of their integration. Our analysis highlights the complementarity of the two fields, while exposing a number of obstacles that can and should be overcome to take full advantage of this merger and thereby accelerate chemical research.

Automated summary

Recent research indicates that machine learning (ML) and high throughput experimentation (HTE) have gained significant attention in the fields of chemistry and engineering, leading to the development of powerful reactivity models and platforms capable of rapidly executing numerous reactions. Although the integration of ML with HTE has not been fully realized, recent developments suggest the potential benefits of their combination, highlighting their complementary nature while also identifying obstacles that need to be overcome for maximizing the impact of this merger on chemical research.