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

11 August 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Buchwald-Hartwig cross-coupling
Long short-term memory neural network
Reaction monitoring
Time series data

Supplementary materials

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

Supplementary weblinks


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