skip to content

Materials Theory Group

 
An icy treasure map

The phase diagram water ice is extremely complex, and has implications ranging from cloud formation to ice skating. Experimentally, 18 crystalline ice phases can be formed under various conditions, and many others have been proposed theoretically. A collaboration between the University of Cambridge and the École Polytechnique Fédérale de Lausanne has combined a high throughput computational search with modern materials informatics techniques to map the crystal structure space of ice, and identify 34 possible new phases.

 

The known stable phases of ice form tetrahedral (four-fold) hydrogen bonded networks of water (H2O) molecules. Databases of tetrahedral networks which have been generated for silica (SiO2) and contain millions of candidates, were used at a starting point to build thousands of possible ice structures. After removing those that were likely to have very high energy, full quantum mechanical density functional theory (DFT) structural optimisations are computed. 

 

The optimised structures (almost 16,000) live in a very highly dimensioned configuration space. To help humans to comprehend this data, the configuration space was mapped onto two dimensions, using “sketch map” - an algorithm which places similar structures near to each other on the map.

 

Mapping uncharted territory in ice from zeolite networks to ice structures

Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard & Richard J. Needs

Nature Communications, 9:2173, 2018

DOI: 10.1038/s41467-018-04618-6 

 

Figure Caption: Nearly 16,000 DFT relaxed ice crystal structures are plotted using the “sketch map” algorithm, which places similar structures near to each other. A “generalised convex hull”  identifies 34 structural diverse structures of ice that might be accessible under the appropriate thermodynamic conditions.

Latest news

Fast and easy exploration of crystal properties using machine-learned Ephemeral Data-derived Potentials

12 January 2024

Machine learning is quickly gaining prominence in the field of computational materials science. In the Materials Theory Group, we develop so-called ‘machine learned interatomic potentials’ (MLIPs), which can describe the...

Structure and colour in nitrogen-doped lutetium hydride

19 December 2023

Superconducting materials have a wide range of applications - from efficient power transmission to the advanced electromagnetics used in MRI machines - due to their loss-free conductivity. Current practical superconductors...

Quantum-induced hydrogen hopping in high-temperature superconducting lanthanum polyhydride

14 April 2023

Figure caption : Quantum effects are essential for hydrogen to dynamically explore different configurations. On the left, we see how the hydrogen atoms cover much larger distances at all temperatures when quantum effects are...

Flat water and ice

26 September 2022

Figure Caption : Pentagonal ice – a two-dimensional form of ice predicted to form when water is squeezed between graphene sheets. Water can be found trapped in nanoscale cavities, for example in biological membranes, or in...

Congratulations Ben Shires!

2 August 2022

Ben completed his PhD viva last week, covering his work on SHEAP , and he will soon be Dr Shires. Congratulations! shires.jpg

Quicker Crystals

14 July 2022

First-principles structure prediction has enabled the computational discovery of materials with extreme, or exotic properties. For example, the dense hydrides, which following computational searches have been found to...

New carbonates uncovered

7 January 2022

A study by Joseph Nelson and Chris Pickard of the Department of Materials Science and Metallurgy, University of Cambridge and the AIMR, Tohoku University, uses structure prediction to exhaustively explore the Ti-C-O and Al-C-O ternary systems.