Joseph Davies Poster

Machine Learning for Yield Prediction for Chemical Reactions Using in situ Sensors

Joseph Davies1, Jonathan Hirst1 and David Pattison2

 1University of Nottingham


Machine learning models were developed to predict product formation from time-series reaction data for ten repeats of a Buchwald-Hartwig coupling reaction.[1] 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 reaction used here was suitable due to the reliable curve shape and profile of the reaction. Mild changes between runs, such as the rate of hydrazine addition, did not have a noticeable impact on prediction. However, large changes may; concentration or temperature could affect the rate of reaction. Future work will investigate how more significant changes affect the accuracy of the model. To assess further the potential and utility of using machine learning to predict product, more examples would need to be examined. This methodology could enable AI augmentation of reaction monitoring to assist synthetic chemists and facilitate a greater understanding of the reaction by identification of correlations between sensor features and reaction outcomes. Insights into the chemistry being performed could also be developed, for example, the correlation between cumulative green and product formation in this work, providing a quantitative description of the colour change in the reaction. The work here presents an example into the insight that can be obtained from applying machine learning methods to sensor data in synthetic chemistry.


[1] J. C. Davies, D. Pattison and J. D. Hirst, J Mol Graph Model, 2023, 118, 108356.