Pinpointing the Location of the Elusive Liquid-Liquid Critical Point in Water

08 May 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The fascinating hypothesis that supercooled water may segregate into two distinct liquid phases, each with unique structures and densities, was first posited in a seminal study by Poole et al. in 1992. This idea, initially based on numerical analyses with the ST2 water-like empirical potential, challenged conventional understanding of water’s phase behavior at the time and has since intrigued the scientific community. Over the past three decades, advancements in computational modeling – particularly through the advent of data-driven many-body potentials rigorously derived from “first principles” and augmented by the efficiency of neural networks – have significantly enhanced the accuracy of molecular simulations, enabling the exploration of the phase behavior of water with unprecedented realism. Our study leverages these computational advances to probe the elusive liquid-liquid transition in supercooled water. For the first time, microsecond-long simulations with chemical accuracy, conducted over several years, provide compelling evidence that water indeed exists in two discernibly distinct liquid states at low temperature and high pressure. By pinpointing a realistic estimate for the location of the liquid-liquid critical point, our study not only advances current understanding of water’s anomalous behavior but also establishes a basis for experimental validation. Importantly, our simulations indicate that the liquid-liquid critical point falls within temperature and pressure ranges that could potentially be experimentally probed in water nanodroplets, opening the possibility for direct measurements.

Keywords

supercooled water
liquid-liquid critical point
molecular dynamics simulations
many-body interactions
machine learning
neural network potentials

Supplementary materials

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Supplementary Information
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Brief overview of the MB-pol and DNN@MB-pol models.
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