Vapor Pressure

Vapor pressure, a fundamental thermodynamic property, is crucial for diverse applications ranging from industrial processes to environmental modeling. Current research focuses on improving vapor pressure prediction accuracy, particularly for complex systems where experimental data is scarce, employing machine learning techniques such as neural networks and leveraging existing physical models (e.g., the Antoine equation) to enhance predictive power and interpretability. These advancements are significant because they enable more efficient simulations and optimizations across various fields, from cold atom experiments to hypersonic flow modeling, ultimately leading to improved process control and design.