Author Correction: Controlling an organic synthesis robot with machine learning to search for new reactivity

Granda, Jarosław M.; Donina, Liva; Dragone, Vincenza; Long, De‐Liang; Cronin, Leroy

doi:10.1038/s41586-019-1218-z

Cited by 2 publications

(1 citation statement)

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“…The use of Artificial Intelligence (AI) for computer-guided materials discovery is an alternative approach that holds the promise to dramatically accelerate the optimization of polymer structure-property relationships, with the opportunity to close the loop between computational and experimental components of the materials discovery pipeline. 6,7 Recent advances in both automated synthetic platforms and machine learning (ML) methods development have enabled experimental systems that provide high quality training data to improve ML models and, at times, are driven by ML recommendations in the areas of small molecule synthesis [8][9][10][11][12][13][14][15][16] and nanomaterial synthesis [17][18][19][20][21][22] . In a recent example, the Doyle group demonstrated a Bayesian optimization platform that allows chemists to iterate between experimentation and ML within their standard synthetic workflows, thus providing open-source tools to increase the efficiency of chemical synthesis.…”

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confidence: 99%

Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

Reis

Gusev

Taylor

et al. 2021

Preprint

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Modern polymer science is plagued by the curse of multidimensionality; the large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19 F MRI agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The non-intuitive design criteria identified by ML, which was accomplished by exploring less than 0.9% of overall compositional space, upended conventional wisdom in the design of 19 F MRI agents and lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.

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