MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning
This tool addresses the problem of cumbersome implementation for researchers and practitioners in chemical engineering and related fields, though it is incremental as it builds on existing ML models.
The authors tackled the problem of technical barriers hindering the practical application of machine learning methods for predicting thermophysical properties by developing MLPROP, an open interactive web interface that allows users to apply advanced ML models without requiring ML expertise, thereby increasing accessibility and enabling integration into existing workflows.
Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like cumbersome implementation in established workflows hinder their application in practice. With MLPROP, we provide an interactive web interface for directly applying advanced ML methods to predict thermophysical properties without requiring ML expertise, thereby substantially increasing the accessibility of novel models. MLPROP currently includes models for predicting the vapor pressure of pure components (GRAPPA), activity coefficients and vapor-liquid equilibria in binary mixtures (UNIFAC 2.0, mod. UNIFAC 2.0, and HANNA), and a routine to fit NRTL parameters to the model predictions. MLPROP will be continuously updated and extended and is accessible free of charge via https://ml-prop.mv.rptu.de/. MLPROP removes the barrier to learning and experimenting with new ML-based methods for predicting thermophysical properties. The source code of all models is available as open source, which allows integration into existing workflows.