Design and Synthesis of Novel Oxime Ester Photoinitiators Augmented by Automated Machine Learning

A new, accelerated design scheme for photoinitiators based on an advanced machine learning framework is studied. Design space for photoinitiators is set by over 120 unique oxime ester compounds synthesized and measured for their photosensitivity. Then, an automated machine learning algorithm is used for rapidly identifying the best quantitative structure–property relationship (QSPR) models among hundreds that are generated, ranked, and validated in an automated fashion to predict photosensitivity. Top-performing models are highly predictive with coefficients of determination of around 0.8 for compounds that are unknown to the models. Visual interpretation of the predictive models based on atom-site contributions offers a clear and intuitive direction to design new photoinitiators. Based on the machine learning-assisted analysis, three new oxime ester compounds were pushed for synthesis and further evaluation as novel photoinitiators. Experimental validation confirms high photosensitivity in all of the newly synthesized candidates. The work demonstrates the value of combining synthesis with the automated machine learning framework as a fast and reliable measure, which provides unbiased insights often hidden in high-dimensional data space.