Ricerc@Sapienza

Despite the simplicity of its molecule, water displays the most complex phase diagram of any single component system, characterized by multiple stable and metastable phases, and with new phases still being discovered. Both the thermodynamic and dynamic properties of water are often at odds with the behaviour of simple liquids. These "water anomalies" have their origin in an experimentally inaccessible region of the phase diagram known as no-man's land, where computer simulations hint at the presence of an elusive liquid-liquid phase separation. Close to this region, water displays both arrested (glassy) and crystalline phases. The characterisation of these phases and of the processes through which they are connected requires a microscopic understanding of the structure of water molecules under extreme conditions. Traditional descriptors of local order, also known as order parameters, are low-dimensional representations of the local environment around a water molecule. While conceptually simple, they are known to have poor spatial resolution, and have lead to misidentifications problems in a large number of previous studies.
In this project we plan to fundamentally change the way water local structures are studied. We abandon the idea of low-dimensional order parameters, and instead describe water with high-dimensional quantities, where information about the real space structure is retained as much as possible. In order to manipulate these quantities we then employ Machine Learning methods, such as supervised and unsupervised Neural Networks.
These new high-dimensional descriptors of water's local environment will then be applied to study the process of ice nucleation under supercooled conditions. We plan to extend our Machine Learning techniques to also characterize the non-equilibrium glassy states of water.