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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Machine Learning

Background:

  • Accurate prediction of thermophysical properties is crucial for chemical engineering and industrial applications.
  • Existing prediction methods, including traditional and machine learning approaches, often suffer from significant errors and poor extrapolation capabilities.
  • Experimental data for many organic compounds are scarce due to cost, safety, or procedural challenges.

Purpose of the Study:

  • To develop improved methods for predicting thermophysical properties of organic compounds.
  • To enhance the extrapolation capabilities of deep learning models by integrating physics-based constraints.
  • To demonstrate the efficacy of physics-informed neural networks (PINNs) for property prediction using surface tension and normal boiling point as case studies.

Main Methods:

  • Developed a multilayered physics-informed neural network (PINN) for predicting parachor (related to surface tension).
  • Utilized a PINN trained on a dataset of 1600 compounds to predict normal boiling points.
  • Incorporated chemistry and physics principles into the neural network training process.
  • Ensured a balanced split of compound types across training, validation, and test sets.

Main Results:

  • The PINN demonstrated improved extrapolation capabilities for deep learning models by incorporating physics-based constraints.
  • For normal boiling point prediction, the PINN achieved a mean absolute error of 6.95 °C on training data and 11.2 °C on test data.
  • Constraining group contributions to be positive further improved predictions on the test set.

Conclusions:

  • Physics-informed neural networks (PINNs) offer a promising approach to enhance the prediction accuracy and extrapolation of thermophysical properties.
  • Integrating physics-based constraints into machine learning models is key to overcoming limitations of traditional methods.
  • The developed PINN approach shows significant potential for improving predictions of various thermophysical properties beyond the studied cases.