Chemical Non-Destructive Evaluation at the Center for Process Analytical Chemistry

Kowalski, Bruce R.; Koch, Julia

doi:10.1007/978-1-4613-0383-1_1

Cited by 1 publication

(1 citation statement)

References 25 publications

(27 reference statements)

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“…18 These methods are typically non-destructive and real-time, offering advantages over traditional sampling methods. [18][19][20] The use of sensors in organic chemistry is an emerging area, fuelled by advances in flow and automated synthesis. [21][22][23] Using sensors inside organic chemistry reactions generates data which could be valuable in the development of machine learning tools to augment the synthesis process, ultimately helping the chemist.…”

Section: Introductionmentioning

confidence: 99%

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

Davies

Pattison²,

Hirst

2022

Preprint

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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.

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