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Professor JONATHAN HIRST's Outputs (4)

AI4Green4Students: Promoting Sustainable Chemistry in Undergraduate Laboratories with an Electronic Lab Notebook (2025)
Journal Article
Nwafor, P. C., Gurung, S., van Krimpen, P., Schnaubert, L., Jolley, K., Pearman-Kanza, S., Willoughby, C., & Hirst, J. D. (2025). AI4Green4Students: Promoting Sustainable Chemistry in Undergraduate Laboratories with an Electronic Lab Notebook. Journal of Chemical Education, https://doi.org/10.1021/acs.jchemed.4c01393

AI4Green is an open-source, machine-learning-powered electronic laboratory notebook (ELN) developed to help chemists mitigate environmental impacts, particularly within the pharmaceutical sector. This study presents AI4Green4Students, a pedagogically... Read More about AI4Green4Students: Promoting Sustainable Chemistry in Undergraduate Laboratories with an Electronic Lab Notebook.

AI4Green: An Open-Source ELN for Green and Sustainable Chemistry (2023)
Journal Article
Boobier, S., Davies, J. C., Derbenev, I. N., Handley, C. M., & Hirst, J. D. (2023). AI4Green: An Open-Source ELN for Green and Sustainable Chemistry. Journal of Chemical Information and Modeling, 63(10), 2895–2901. https://doi.org/10.1021/acs.jcim.3c00306

An Electronic Laboratory Notebook (ELN) combining features, including data archival, collaboration tools, and green and sustainability metrics for organic chemistry, is presented. AI4Green is a web-based application, available as open-source code and... Read More about AI4Green: An Open-Source ELN for Green and Sustainable Chemistry.

AI4Green: An Open-Source ELN for Green and Sustainable Chemistry (2023)
Preprint / Working Paper
Boobier, S., Davies, J., Derbenev, I., Handley, C., & Hirst, J. AI4Green: An Open-Source ELN for Green and Sustainable Chemistry

An Electronic Laboratory Notebook (ELN) combining features, including data archival, collaboration tools, and green and sustainability metrics for organic chemistry, is presented. AI4Green is a web-based application, available as open-source code and... Read More about AI4Green: An Open-Source ELN for Green and Sustainable Chemistry.

Kernel Methods for Predicting Yields of Chemical Reactions (2021)
Journal Article
Haywood, A. L., Redshaw, J., Hanson-Heine, M. W., Taylor, A., Brown, A., Mason, A. M., Gärtner, T., & Hirst, J. D. (2022). Kernel Methods for Predicting Yields of Chemical Reactions. Journal of Chemical Information and Modeling, 62(9), 2077-2092. https://doi.org/10.1021/acs.jcim.1c00699

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in... Read More about Kernel Methods for Predicting Yields of Chemical Reactions.