DeepFuel-kit/Publications

Peer-reviewed Publications

An AI-driven de novo design and optimisation of sustainable aviation fuels

Rodolfo S.M. Freitas, Fengqi You, Zhihao Xing, Fernando A. Rochinha, Roger F. Cracknell, Daniel Mira, Alessandro Parente, Kai H. Luo, Jinyue Yan, Xi Jiang

Energy and AI, 2026. https://doi.org/10.1016/j.egyai.2026.100791

Pathways to sustainable fuel design from a probabilistic deep learning perspective

Rodolfo S. M. Freitas, Zhihao Xing, Fernando A. Rochinha, Roger F. Cracknell, Daniel Mira, Nader Karimi, Xi Jiang

Advances in Applied Energy, 2025. https://doi.org/10.1016/j.adapen.2025.100226

A data-driven multi-level simulation framework for ammonia-syngas combustion

Zhihao Xing, Rodolfo S. M. Freitas, Xi Jiang

Chemical Engineering Journal Advances, 2025. https://doi.org/10.1016/j.ceja.2025.100960

Machine learning-driven multi-objective optimisation of ammonia co-firing with highly reactive fuels

Zhihao Xing, Rodolfo S. M. Freitas, Xi Jiang

Energy Conversion and Management, 2025. https://doi.org/10.1016/j.enconman.2025.120071

Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia

Zhihao Xing, Rodolfo S. M. Freitas, Xi Jiang

Energy and AI, 2024. https://doi.org/10.1016/j.egyai.2024.100454

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

Rodolfo S. M. Freitas, Xi Jiang

Energy and AI, 2024. https://doi.org/10.1016/j.egyai.2024.100385

Neural network potential-based molecular investigation of pollutant formation of ammonia and ammonia-hydrogen combustion

Zhihao Xing, Xi Jiang

Chemical Engineering Journal, 2024. https://doi.org/10.1016/j.cej.2024.151492

Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models

Rodolfo S. M. Freitas, Ágatha P.F. Lima, Cheng Chen, Fernando A. Rochinha, Daniel Mira, Xi Jiang

Fuel, 2022. https://doi.org/10.1016/j.fuel.2022.125415