ML models for partition functions: from the prediction of thermodynamic properties to the exploration of transition pathways

We discuss our recent work on the development of machine learning (ML) models for the prediction of partition functions. To this end, we carry out computationally-intensive flat histogram Monte Carlo simulations, based on a Wang-Landau sampling scheme, to obtain the partition function for atomic and molecular systems. The simulation results for the partition functions are then gathered in datasets that allow us to train and validate ML models. This, in turn, leads us to build artificial neural networks models for the prediction of partition functions for a broad range of conditions. We demonstrate the accuracy and reliability of the ML models by showing the ability of the ML-partition function to predict thermodynamic properties for single component-systems, mixtures, confined fluids. Furthermore, we show that the ML-partition function can be leveraged to build reaction coordinates for the exploration of phase transition pathways.