About AI4Green
Empowering scientists to make sustainable choices
AI4Green is a suite of software that helps chemists manage their research data, work more efficiently, securely collaborate around the world, and be more sustainable.
Mission Statement
AI4Green is dedicated to advancing sustainable chemistry through cutting-edge, data-driven methods, open-source innovation, and the promotion of FAIR data principles. Developed at the University of Nottingham by Professor Jonathan Hirst's research group, our software equips researchers and industry professionals with the tools to make greener, data-informed decisions. This transformative initiative is proudly supported by a grant from the Royal Academy of Engineering.
AI4Green ELN
The AI4Green ELN uses an intuitive structure of workgroups and workbooks to enable seamless collaboration whilst protecting your data.
Our platform provides researchers with powerful tools to:
- ✓ Track and optimise reaction conditions
- ✓ Assess and reduce environmental impact
- ✓ Collaborate seamlessly and securely with colleagues
- ✓ Make data-driven decisions
- ✓ Automatically retrieve hazard codes
- ✓ Generate professional-standard COSHH assessment matrices
- ✓ Automatically calculate molar amounts
- ✓ Quickly search reactions by structure
- ✓ Draw reactions in an integrated chemical sketcher
- ✓ Join workgroups by QR code
Sustainability Tools
Green Metrics Calculator
Automatic calculation of holistic reaction sustainability metrics including solvent sustainability, atom economy, and mass efficiency.
Informed by the CHEM21 consortium, our integrated sustainability metrics helps researchers make informed decisions about their reaction conditions, leading to more sustainable outcomes and reduced environmental impact.
Solvent Substitution
Solvent Guide
Interactive flashcards which provide visual rationale for the sustainability of a solvent and recommended greener alternatives.
Make more informed choices about solvents with our simple customisable and colour-coded system.
Solvent Surfer
Interactive tool for exploring and comparing different solvents based on their physical properties and environmental impact for different reaction classes.
Drag points to impart your own knowledge on the tool and tailor for your specific use case to identify greener alternatives.
Retrosynthesis Planning
AI-powered retrosynthesis route planning tool with condition prediction to design more sustainable synthetic routes.
Rapidly generate and analyse potential synthetic pathways considering factors such as solvent sustainability, atom economy, and reaction safety.
Publications
AI4Green: An Open-Source ELN for Green and Sustainable Chemistry
S. Boobier, J. C. Davies, I. N. Derbenev, C. M. Handley, and J. D. Hirst, J. Chem. Inf, 2023, 63 (10), 2895-2901
View PublicationSolvent Flashcards: A Visualisation Tool for Sustainable Chemistry
J. Heeley, S. Boobier, and J. D. Hirst, J. Cheminform., 2024, 16 (1), 60.
View PublicationInteractive Knowledge-based Kernel PCA for Solvent Selection
S. Boobier, J. Heeley, T. Gaertner, and J. D. Hirst, ACS Sustainable Chem. Eng., 2025.
View PublicationRelated Software
AI4Green4Students
AI4Green4Students is a version of our platform designed specifically for use in undergraduate laboratories.
Instead of automatically completing, students must input their own hazard codes and molar
calculations which are checked automatically by the system.
It teaches students data management, green chemistry principles, and software skills alongside
their practical laboratory work.
The assessor view provides a superior ability to monitor and provide feedback on their students work.
The platform includes:
- • Simplified interface for educational settings
- • Learning resources
- • Teacher management tools and assignment features
- • Focus on fundamental green chemistry concepts
- • Green chemistry metric calculators
Get Involved
Visit our GitHub Repository for full source code and instructions on how to build your own instance of AI4Green.
For any queries, feedback, or suggestions, please contact us: ai4green@nottingham.ac.uk