Machine learning-assisted chemical design of highly efficient deicers

Abstract

Abstract The use of deicers in urban areas, on runways and aircrafts has raised concerns about their environmental impact. Understanding the ice-melting mechanism is crucial for developing environmentally friendly deicers, yet it remains challenging. This study employs machine learning to investigate the ice penetration capacity (IPC) of 21 salts and 16 organic solvents as deicers. Relationships between their IPC and various physical properties were analysed using extreme gradient boosting (XGBoost) and Shapley additive explanation (SHAP). Three key ice-melting mechanisms were identified: (1) freezing-point depression, (2) interactions between deicers and H2O molecules and (3) infiltration of ions into ice crystals. SHAP analysis revealed different ice-melting factors and mechanisms for salts and organic solvents, suggesting a potential advantage in combining the two. A mixture of propylene glycol (PG) and sodium formate demonstrated superior environmental impact and IPC. The PG and sodium formate mixture exhibited higher IPC when compared to six commercially available deicers, offering promise for sustainable deicing applications. This study provides valuable insights into the ice-melting process and proposes an effective, environmentally friendly deicer that combines the strengths of organic solvents and salts, paving the way for more sustainable practices in deicing.

Keywords

• Deicer
• Machine learning
• Boosting
• SHAP
• Ice-melting mechanism
• Deicing