Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption

Generalized, Adsorbent-Agnostic, Artificial Neural Network Framework for Rapid Simulation, Optimization, and Adsorbent Screening of Adsorption Processes

Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption-Based Postcombustion CO₂ Capture

Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods

Physics-Based Neural Networks for Simulation and Synthesis of Cyclic Adsorption Processes