Developing new materials – or improving existing ones – is a time-consuming process of trial and error. Thanks to CASTEP, software developed at Cambridge and based on quantum mechanics is taking the guess work out of R&D.
Making materials better is a key part of technology innovation, but doing so often takes decades. Now, software based on quantum mechanics and originally developed by physicists at Cambridge is helping R&D teams speed up the search for new materials. Called CASTEP, the modelling code is widely used in many industries – from chemical and semiconductor manufacture to oil and gas.
CASTEP has been licensed to Cambridge-based software company Accelrys – now BIOVIA – since 1995 and in 2013 passed $30m in sales.
Used in tandem with ‘real’ experiments, the ‘virtual’ experiments that CASTEP enables mean that as well as boosting efficiency of the R&D process, the code can help pinpoint sources of product failures.
Key to its widespread use is its accessibility: the code’s interface means it can be used by any academic or bench-top scientist, allowing its adoption across many industries. UK academics can use the code for free; as a result, hundreds of peer-reviewed papers based on CASTEP calculations are published every year.
An academy of sceptics
From better solar cells to more efficient catalysts, technological innovation is often based on materials. Traditionally, development of better materials has been based on trial and error, a process of incremental improvement that can take decades to deliver.
Computation and simulation are drivers of economic growth, so models that reliably describe materials, and which can be used alongside ‘real’ experiments, would o er many advantages to many industries.
In theory, the part of quantum mechanics known as density functional theory should be able to describe anything from a single atom of silicon to a Boeing 747. However, quantum mechanical equations for things more complex than a single particle are hugely complex.
When Professor Mike Payne joined the field in the 1980s, quantum mechanics could describe nothing more complex than two atoms of silicon, and the scientific world was sceptical that density functional theory would ever have relevance outside academia.
Breakthroughs in quantum mechanics and density functional theory, however, transformed the field and paved the way for CASTEP.
Rigorous, robust and reliable
Breakthroughs in quantum mechanics and density functional theory meant the methods Payne had pioneered were capable of modelling not just a few atoms, but many atoms across the Periodic Table.
As well as playing a key role in the underlying physics, Payne and CASTEP’s other developers were early adopters of parallel computing, giving the software the memory it needed to perform its complex calculations on very large systems containing hundreds of atoms.
The code was completely re-written – by six researchers in their spare time – between 2000 and 2003.
Because it is a full-featured material modelling code based on a first-principles quantum mechanical description of electrons and nuclei, CASTEP helps R&D teams understand very complicated problems.
Used alongside ‘real’ experiments, the ‘virtual’ experiments that CASTEP performs means R&D teams can model anything based on atoms, understand how it works – and then generate ideas about how to improve it.
Already used by catalyst companies to gain a clearer understanding of how catalysts work – and therefore how best to improve them – and by the semiconductor industry as a tool to diagnose the source of impurities in the manufacturing process, a major area of potential growth for CASTEP is in the pharmaceutical sector.
Pharmaceutical firms rely on patents to protect the large sums of money they invest in developing new drugs. Patenting the right crystal structure, however, is key.
Combined with solid-state NMR, CASTEP can ensure all active crystal structures are identified and protected.
Computation and simulation are drivers of economic growthProfessor Mike Payne, Cavendish Laboratory
Over and over again people told us what we couldn’t do with this methodology; over and over again we proved them wrongProfessor Mike Payne, Cavendish Laboratory