Support for the UK Car-Parrinello Consortium

Lead Research Organisation: University College London
Department Name: Physics and Astronomy

Abstract

Many technological advances in modern day life are dependent upon the development of new materials or better control and understanding of existing materials. Understanding the detailed properties of materials has therefore never been more important. The development of high quality computer simulation techniques has played an increasing significant role in this endeavour over recent years. The UK has been at the forefront of this new wave, and the UKCP consortium has played an important part, in both developing computer codes and algorithms, and exploiting these new advances to increase our understanding of many industrially relevant materials and processes.The research described in this proposal will make significant impacts on many areas of future technology, such as the development of new materials for hydrogen storage which will be necessary for zero-pollution cars in the future, the development of new materials for alternative computer memory technologies, and the development of new carbon-based nano-sized electronic components that could replace silicon altogether.Other parts of this proposal seek to develop new algorithms and theoretical improvements that will increase our simulation abilities, either by increasing the accuracy and reliability of calculations, or by enabling us to simulate bigger systems for longer. These will enable the next generation of simulations and further widen our computational horizons.The research proposed does not easily fit into any of the traditional categories of 'physics' or 'chemistry' etc. Instead, the UKCP is a multi-disciplinary consortium using a common theoretical foundation to advance many different areas of materials-based science.

Publications

10 25 50
 
Description This grant provided access to high performance computing (HECTOR), supporting my Leadership Fellowship (EP/G007489/2).
Exploitation Route ?
Sectors Aerospace

Defence and Marine

Agriculture

Food and Drink

Chemicals

Construction

Digital/Communication/Information Technologies (including Software)

Electronics

Energy

Environment

Healthcare

Manufacturing

including Industrial Biotechology

Pharmaceuticals and Medical Biotechnology

Security and Diplomacy

Transport

 
Title AIRSS 
Description 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. The AIRSS package is released under the GPL2 licence. 
Type Of Technology Software 
Year Produced 2017 
Impact It appears that researcher are routinely using AIRSS. 
URL https://www.mtg.msm.cam.ac.uk/Codes/AIRSS