Predicting Thermodynamics of Liquid Water from Time Series Analysis

TL;DR

This paper introduces a novel method using time series analysis and AI to predict thermodynamic properties of liquid water from molecular dynamics data, offering new insights beyond traditional statistical mechanics.

Contribution

It presents a new approach that interprets thermodynamics through time series analysis of hydrogen bond networks, enabling predictions beyond sampled regions.

Findings

Temporal evolution of HBN topology encodes thermodynamics

AI uncovers patterns in microscopic data for property prediction

Method extends thermodynamic predictions beyond direct simulations

Abstract

Thermodynamics, introduced over two centuries ago, remains foundational to our understanding of physical, chemical, biological, and engineering systems. Its principles are traditionally grounded in the statistical mechanics framework, which explains macroscopic behavior from microscopic states. In this work, we propose an alternative approach that interprets thermodynamic behavior through the lenses of time series analysis, an approach commonly used in other fields, including finance, climate, and signal processing. We perform classical molecular dynamics simulations of liquid water, the most complex, anomalous, and important substance known, over a wide range of its phase diagram. By examining the temporal evolution of the hydrogen bond network (HBN) topology, we demonstrate that the dynamics of microscopic topological motifs populating the HBN encode the system's macroscopic thermodynamic behavior. Furthermore, our approach enables the prediction of thermodynamic properties in regions beyond those directly sampled in our simulations. We achieve this result by leveraging artificial intelligence to uncover patterns in temporally resolved data that are often lost through conventional averaging. This work offers new insights into the fundamental behavior of water and network-forming materials more broadly, establishing a new paradigm for understanding material properties beyond the classical confines of statistical mechanics.