Work

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

Year: 2022

Type: article

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 min 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.

Source: Journal of Molecular Graphics and Modelling

DOI: 10.1016/j.jmgm.2022.108356

Authors: Joseph C. Davies, David Pattison, Jonathan D. Hirst

Institution: University of Nottingham

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,310

Funder: Royal Academy of Engineering