Machine Learning Models for Partition Functions: Predicting Thermodynamic Properties and Exploring Transition Pathways

In our recent work, we have focused on developing machine learning (ML) models for predicting partition functions. To do this, we carried out computationally intensive flat histogram Monte Carlo simulations based on a Wang-Landau sampling scheme. These simulations helped us to obtain the partition function for atomic and molecular systems. The results of these simulations were then combined into datasets that allowed us to train and validate ML models. We used these models to build artificial neural networks capable of predicting partition functions for a wide range of conditions.



We demonstrated the accuracy and reliability of our ML models by showing their ability to predict thermodynamic properties for single-component systems, mixtures, and confined fluids. Additionally, we showed that our model could be used to create reaction coordinates for exploring phase transition pathways.