Unraveling Water’s Mysteries

: Structure, Dynamics, and Neural Network Investigations

Abstract

Although water is the most essential substance for humans and every form
of it has been the subject of intense study, it still presents mysteries that the
scientific community has yet to unravel beyond reasonable doubt. This thesis
examines two of the most challenging regions of the phase diagram: the stable solid
phases at high pressures and temperatures and the anomalies observed in liquid
water when cooled below the melting temperature.
The initial section of this thesis presents a topological and dynamic characterisation
of the recently observed plastic phase, which exists at the boundary between the
crystalline phase of ice VII and the liquid phase. The difficulties inherent in conducting
experiments under such extreme conditions have resulted in a poor understanding
of this region. By mean of computational tecnique, we present an analytical model
developed to specifically describe the rotational dynamics within this plastic phase,
thereby providing new insights into its unique behaviour.
The second part of the study addresses the anomalies of water under supercooled
conditions. This research employs a machine learning approach to investigate the
patterns between the topology of hydrogen-bond networks and the pronounced
thermodynamic anomalies. The study focuses on key observables, including heat
capacity, isothermal compressibility, and density, demonstrating strong correlations
that enhance our understanding of the anomalous behaviour of supercooled water.

Date of Award	10 Dec 2024
Original language	English
Awarding Institution	University of Bristol
Supervisor	Farhad Babaee (Supervisor), Tanniemola B Liverpool (Supervisor), Fausto Martelli (Supervisor) & John Russo (Supervisor)