skip to content

Materials Theory Group

 
Complex atomic networks
Complex network analysis is a computational tool that be been used to study problems as diverse as uncovering hidden social groupings and divining new tasty recipes. A collaboration between researchers in the University of Cambridge Materials and Physics Departments have introduced a way to use complex network analysis to break materials down into their constituent fragments (or modules), and then put them back together in different ways, assisting the computational discovery of new materials.
 
A community detection algorithm is applied to a complex atomic network (built up from the distances between pairs of atoms). The resulting communities, or modules, are examined and the most simple solutions (in terms of the amount of information required to describe them) are selected. This approach is applied to a variety of crystal structures, and used to uncover potential polytypism in a dense phase of boron. It is suggested that the method could be applied to biomolecular systems.
 
Revealing and exploiting hierarchical material structure through complex atomic networks
Sebastian E. Ahnert, William P. Grant and Chris J. Pickard
npj Computational Materials, (2017) 3:35; doi:10.1038/s41524-017-0035-x

Latest news

Successful Gordon Research Conference on Materials at High Pressure

27 September 2024

The Materials Theory Group was well represented at the Gordon Research at High Pressure Conference in Holderness, New Hampshire, from 14-19 July. This year, the conference was chaired by our group leader, Prof. Chris Pickard...

Predicting a potentially synthesisable ambient-pressure high-Tc superconducting hydride

10 May 2024

Superconductors are a class of materials which show zero resistance and the expulsion of magnetic fields below a critical temperature, T c . These materials have a wide range of applications, including fusion reactors where...

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