A team of researchers at the University of Nottingham has developed a new AI tool to encourage chemists and chemical engineers to make processes more sustainable.
A team led by Professor Jonathan Hurst, Royal Engineering Chair of Emerging Technology, has strengthened well-known AI algorithms to improve predictions for retrosynthesis, a framework that chemists use to best try their best to start molecules from easily accessible starting materials. Their research is published this week in the Journal of Chemical Information and Modeling.
This method is also accessible from the open source AI4Green Electronic Lab Notebook, available at https://ai4green.app.
From creating new effective drug treatments to new batteries, chemical processes are essential to the invention of new molecules and materials to promote innovation across different industries to provide sustainable energy. However, these processes are toxic and can damage the environment, and it is important for chemists to find new ways to become more sustainable. AI offers easy sharing solutions. I hope that everyone who works in the chemistry will use this new framework.
An unveiled by the AI4Green team earlier this year, a new interactive machine learning approach will help chemists choose the medium in which chemistry is implemented. Choosing a more sustainable solvent is a key factor in mitigating the environmental impact of chemical processes.
Training next-generation chemists is essential in the use of AI tools and digital research environments. The AI4GREEN team has developed a pedagogical version of the AI4Green Electronic Lab Notebook in collaboration with ThePhysical Sciences Data Infrastructure, funded by the EPSRC.
The Electronic Lab Notebook is used by third-year undergraduates at the Educational Institute of Chemistry to promote sustainable chemistry recognition. This work is described in this month's Journal of Chemistry Education publication.
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