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with mineral surfaces, where there might be a hydrated surface or solvents present. Such insight is very useful to industries such as oil and gas. Te group also looks at the interactions

that occur in chemical catalysis, as well as new materials research. ‘With computers getting more powerful and

models getting more sophisticated, we can now get very good size and timescale agreement. Simulation allows us to see something that experimentation can’t allow you to see, for example, behaviour at different layers in a material.’ Another example of this is being able to study

materials at temperatures, pressures and other real-world conditions that are hard to replicate in the lab, for example, to model matter as you go down through the Earth’s crust. Modelling, Greenwell said, also enables you

to screen many properties quite quickly and to isolate two or three variables, which is hard to do in a lab experiment. Te type of modelling approach depends

on the system being modelled, he said. For example, he uses quantum mechanics-based code to study catalysis but this can only be applied to study a very small area. Most of the modelling work in his group, he said, uses molecular mechanics. ‘Most interactions don’t involve bond breaking and this is a simpler model, so much quicker,’ he explained. He also noted a good availability of

modelling code, both from academia and industry. His group, for example, uses the CASTEP package, developed by a group of UK academics, and LAMMPS from Sandia National Laboratory. ‘We write some things but there’s no real need to develop our own code,’ he added. Gilberto Teobaldi of the Stephenson Institute

for Renewable Energy at the UK’s University of Liverpool is another user of computational techniques to study new materials, using the ONETEP package and other academic soſtware (DL_POLY, TINKER). His research focuses on the theory and atomistic-modelling of photo-electro-chemical interfaces and on their potential for renewable energy generation (photovoltaics), storage (batteries and supercapacitors), and more efficient use (photo- catalysis). ‘At the moment there are many challenges

that cannot be addressed experimentally, for example, direct and atomically resolved insight into the functioning of buried interfaces in batteries,’ he said. ‘Atomistic modelling is crucial in providing such insight, and expanding our understanding of what causes batteries to degrade.’ Teobaldi uses both ab initio (mostly density


Modelling the density gradient in a compressed powder IN ADDITION TO


functional theory) and force-field based atomistic modelling. ‘By solving, to a different level of accuracy, the nuclear and electronic equation of motions of model-systems, both methods are capable to provide direct access to the atomistic-parameters that control the (mal-) functioning of energy-relevant materials and interfaces,’ he explained.

Scaling up Te use of computational modelling is not restricted to the early stages of a project either, according to Ravi Aglave. He is CPI industry sector manager for CD-adapco, which provides computational fluid dynamic tools aimed at the chemical industry. He explained: ‘Chemists are all the time

developing things in the lab on a small scale but we have to take them to the real world, where tens of thousands of tonnes might need to be made.’ Tis scaling up is not straightforward and

requires significant engineering. For example, if a reaction generates heat in a test tube, the surrounding air will generally dissipate the heat easily. Once the reaction is no longer on the scale of a few grams but a few tonnes, that heat

becomes much more of a challenge. As Aglave noted, without careful engineering, it could cause a runaway reaction and explosions. Te answer is to try to design systems that avoid such problems and here computers can help. CD-adapco’s soſtware enables the chemical

industry, and others, to model how fluids move, chemical reactions, and mass energy transfer. For this, modellers feed in the geometries and boundaries of the vessels in a chemical plant. Tey also add into the model some input and output conditions from the laboratory studies. Tese include the chemical species involved, heat released, reactions, intermediates and any unwanted side products. ‘People are realising the power and value

of this type of simulation,’ observed Aglave. ‘Someone who was doing five to 10 simulations per year 10 years ago is probably doing hundreds per year now.’

Computing power Part of the reason for this huge increase is progress made in computing power. ‘People usually utilise several cores to achieve solutions,’ noted Aglave. ‘As parallelisation and computing power have increased, the cost of carrying out these type of calculations has gone down.’ Both Greenwell and Teobaldi use a range of

high-performance computing (HPC) resources, based in Europe and North America. ‘Te advent of codes able to partition

simulations over many processors with linear or near linear scaling, coupled to the advent of fast interconnects and high-performance

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