Research in the Zahrt group focuses on creating new tools to advance organic synthesis by integrating automation and machine learning workflows to enhance molecular function, reaction efficiency, and sustainability. We develop and use active learning strategies for catalyst design, reaction conditions, and other molecular properties. To accelerate the implementation of these algorithms, we pair them with automated experimentation platforms. Applications of these approaches include the exploration of higher order solvent mixtures as a new optimization domain in organic chemistry, a new algorithmic approach to catalysts design paired with on-demand catalyst synthesis, and automated design and synthesis of biologically active molecules, and the exploration of the new synthetic methodology. Current methodology exploration includes innovations in the cation flow technology, using engineering solutions to overcome chemical incompatibilities to achieve one-pot transformations that would not typically be viable through other means.