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
A data-driven multi-level simulation framework for ammonia-syngas combustion
Zhihao Xing, Rodolfo S. M. Freitas, Xi Jiang
Machine learning-driven multi-objective optimisation of ammonia co-firing with highly reactive fuels
Zhihao Xing, Rodolfo S. M. Freitas, Xi Jiang
Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia
Zhihao Xing, Rodolfo S. M. Freitas, Xi Jiang
Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship
Rodolfo S. M. Freitas, Xi Jiang
Neural network potential-based molecular investigation of pollutant formation of ammonia and ammonia-hydrogen combustion
Zhihao Xing, Xi Jiang
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
