All Outputs (3)

Machine learning insights into predicting biogas separation in metal-organic frameworks (2024)
Journal Article
Cooley, I., Boobier, S., Hirst, J. D., & Besley, E. (2024). Machine learning insights into predicting biogas separation in metal-organic frameworks. Communications Chemistry, 7, Article 102. https://doi.org/10.1038/s42004-024-01166-7

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation pro... Read More about Machine learning insights into predicting biogas separation in metal-organic frameworks.

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)
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.