Retention time prediction of emerging contaminants via transfer learning with graph neural networks

Key Points

• Retention time prediction was effectively achieved using transfer learning methods, showcasing the potential of advanced algorithms.
• The model utilized graph neural networks to analyze the structure of emerging contaminants for enhanced prediction accuracy.
• Data was derived from a comprehensive dataset involving various emerging pollutants, enabling the model to learn complex relationships.
• Findings suggest that utilizing AI for predicting retention times may enhance water quality management efforts.