Theoretical and Computational Chemistry

Machine learning for yield prediction for chemical reactions using in situ sensors

Authors

Abstract

Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including temperature, pressure, and colour. Colour was a good predictor of product formation for this reaction and machine learning models were able to learn which of the properties were important. Predictions for the current product formation (in terms of % yield) had a mean absolute error of 1.2%. For predicting 30, 60 and 120 minutes ahead the error rose to 3.4, 4.1 and 4.6%, respectively. The work here presents an example into the insight that can be obtained from applying machine learning methods to sensor data in synthetic chemistry.

Content

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Supplementary material

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Supplementary Information
Supplementary material providing additional detail on the dataset and its analysis

Supplementary weblinks

Github repository
Github repository containing code and data for replicating the work reported