skip to content

Materials Theory Group

 

AIRSS Banner

Ab Initio Random Structure Searching

Ab initio Random Structure Searching (AIRSS) is a very simple, yet powerful and highly parallel, approach to structure prediction. The concept was introduced in 2006 and its philosophy more extensively discussed in 2011.

Random structures - or more precisely, random "sensible" structures - are generated and then relaxed to nearby local energy minima. Particular success has been found using density functional theory (DFT) for the energies, hence the focus on "ab initio" random structure searching. The sensible random structures are constructed so that they have reasonable densities, and atomic separations. Additionally they may embody crystallographic, chemical or prior experimental/computational knowledge. Beyond these explicit constraints the emphasis is on a broad, uniform, sampling of structure space.

AIRSS has been used in a number of landmark studies in structure prediction, from the structure of SiH4 under pressure to providing the theoretical structures which are used to understand dense hydrogen (and anticipating the mixed Phase IV), incommensurate phases in aluminium under terapascal pressures, and ionic phases of ammonia.

The approach naturally extends to the prediction clusters/molecules, defects in solids, interfaces and surfaces (interfaces with vacuum).

The AIRSS package is tightly integrated with the CASTEP first principles total energy code. However, it is relatively straightforward to modify the scripts to use alternative codes to obtain the core functionality, and examples are provided.

Contact: airss@msm.cam.ac.uk

 

Obtaining AIRSS

The AIRSS package is released under the GPL2 licence. You can download the full source code below. Note that you will require a Unix-like environment in order to build the code. Windows users can install a Linux subsystem if required. Build instructions are contained within the source:

Download current version

As well as the full source, you can access an online version of the AIRSS code which lets you try some of the AIRSS examples in-browser. Read more about this online version in the associated news item, or access it below:

Access the online version of AIRSS

 

Documentation

The AIRSS source contains a number of worked examples to assist the user in learning how to use the code. Online AIRSS documentation is also under construction at https://airss-docs.github.io/ (with thanks to James Walsh, UMass Amherst)

Ephermeral data derived potentials

From v0.9.3 AIRSS can exploit EDDPs to potentially dramatically accelerate structure search. The EDDP package can be downloaded here.

History

Earlier versions of AIRSS:

AIRSS Version 0.9.3

AIRSS Version 0.9.1

AIRSS Version 0.9.0

 

Latest news

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.

Postdoc in High Temperature Conventional Superconductivity

5 January 2022

Applications are invited for a postdoctoral research position with Professor Chris Pickard at the University of Cambridge. Recent advances in computational methods have raised the prospect of the in silico design of high...

Visualising potential energy surfaces using dimensionality reduction

25 November 2021

Computational structure prediction has emerged as a highly successful approach to the discovery of new materials. Candidate structures are created by constructing the most stable configurations that can be adopted by a given...

The elements of life under pressure

1 July 2021

First-principles structure prediction sheds light on high-pressure compounds formed from carbon, hydrogen, nitrogen and oxygen.