About CNthesizer
CNthesizer is a machine-learning-based platform developed to support fuel property prediction, screening, optimisation, and molecular design for compression ignition fuels.
Project Overview
Fourth-year integrated master group project at University College London.
CNthesizer was developed as part of a fourth-year integrated master group project at University College London. The project focuses on the use of machine learning, molecular data, optimisation, and generative design methods to support the discovery and evaluation of cleaner fuel candidates for compression ignition engines.
Project Aim
A unified framework for fuel prediction, optimisation, and candidate generation.
The aim of this project is to support the efficient exploration of compression ignition fuel design space by developing a unified, data-driven framework that predicts key fuel properties, performs screening and optimisation, and identifies promising fuel candidates and additives.
Contributors
Project team members and academic support.
Team Members
Salvina Zahir, Maimoona Rashid, Nureena Nadia Mohamad Azman, Intan Syazana Ramli, Yangyang Huang
Supervisor
Prof. Paul Hellier, University College London (UCL)
Project Advisor
Sergey Anufriiev
Former PhD student of Prof. Paul Hellier who provided regular technical guidance and project support.
GitHub Repository
The full source code and project files are available through the project repository.