Machine-learning tool mines lab notebooks for lessons from failed reactions

A team of researchers have created a machine-learning tool to analyze failed chemical reactions. The system is designed to sift through misfires in researchers’ lab notebooks in search of insights that can support more accurate predictions about what reactions are likely to work in the future. Typically, there is a disparity between the influence of successful and failed reactions. While details of what works spread through publication in research literature, the lessons that can be learnt from failed reactions remain hidden in the lab notebooks of individual researchers. Now, a team from Haverford College have set about trying to develop a tool that can guide material synthesis choices by unlocking these insights. “We used information on ‘dark’ reactions–failed or unsuccessful hydrothermal syntheses–collected from archived laboratory notebooks from our laboratory, and…


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