DATA ANALYSIS: MATERIALS

elastomers. Reversible Addition-

Fragmentation chain Transfer (RAFT) processes, developed by Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO), are at the forefront of developments here. CSIRO’s solution, both in the case

of RAFT and also more widely, is the E-Workbook research data management system from IDBS – more specifically, its extended ChemBook variant. E-Workbook is one of the expanding number of applications (Wolfram’s Workbench, used by many materials modelling groups for Mathematica, is another relevant example) that look to the open source Eclipse Foundation for a flexible environment that encourages extension. Fitzgerald and his coauthors, quoted earlier, consider the computational scalability issue against the background of Accelrys Pipeline Pilot. Either way, the inevitable trend is the same as in many other areas: materials science is inseparable from cheminformatics. Quite apart from the efficiency in analysis itself, automation also eliminates human error and the resource absorption of shifting data or results from one platform to another. The essence of such automation is to

capture best practice in a ‘workflow’ or ‘pipeline’ (the difference lies in how the tasks are handled, with pipelines making more efficient use of resources), which eliminates waste and delay, unifies formats and standards, handles iteration flexibly to reach desired end states without intervention, and

deliver results to whatever forms are needed – typically databases, reports and software code structures. George Fitzgerald is Accelrys’ senior marketing manager for materials, and points to the time and efficiency advantages of moving experiments and analyses inside a single virtual management container. A mass of options can be screened much more rapidly in software than in physical experiment, narrowing them down to the top 10 or 20 per cent on which further attention can be focussed. Materials science, as shown by some of the

illustrations already quoted, doesn’t have to mean constructional materials like carbon fibre and Fitzgerald offers very different examples. In a collaboration with Mitsubishi, stabilisation of a lithium electrolyte involving six thousand or so combinatorial library calculations. Informatics automated the process, Pareto assessments filtering out about a hundred candidates for further examination. L’Oréal, whose materials range from pigments through oils to antioxidants, is merging half a dozen internal databases into Pipeline Pilot, which brings integral understanding of molecules and substructure searches. Unilever, by integrating data, informatics and analysis, reaped a 90 per cent efficiency gain. In a study searching for platinum catalyst replacements in fuel cells, complexity is multiplied by alloy crystal structures.

In search of perfect chocolate

Problem: How do you create a product that has just the right mouth-feel? How could you modify an existing process to change product texture? How would you assure quality control of the new product? Solution: Data automation with Pipeline Pilot Imaging Collection It’s well known that the texture, chew,

and mouth-feel of food can be altered by the presence of bubbles. While bubbles do not contribute to food value, they do impact customer perception and marketability of a food product. Work by Nestlé and the University of Reading showed the relationship between bubble sizes and sensory ratings. Chocolates with larger voids were perceived

as less hard and less creamy. Smaller bubbles, on the other hand yield products that are perceived as creamier and harder.

www.scientific-computing.com

Depending on the target customer, a

company can tailor mouth-feel with processes that adjust – among other factors – the bubble sizes. Here software for image analysis plays a key role: nobody wants to count bubbles by hand. Software will be faster, more repeatable, and less error prone than human analysis of images. It makes it possible to test samples regularly and automatically alert the human operators to a problem in real time. R&D data integration delivers the added

benefit of correlating all known production factors with any failures – whether in bubble distribution or elsewhere. Trend analysis can identify factors such as particular batches of ingredients or operating conditions that give rise to unacceptable levels of low quality product. Predictive analytics can quantify the relationship between bubble size and mouth

Carbon fibre mesh. (Image: Asma Hassan)

By finding high level trends, groups and classes of calculations can be eliminated and correlation of energy level with lattice size gives a first approximation, which in turn minimises experimentation. Similarly, research by BASF, General Motors and the US Department of Energy into reburning of automotive exhaust hydrocarbons involves thousands of catalytic materials, but information management reduces the field to manageable size. Medicine is a major contributor and consumer of materials expertise, across a range from structural rebuilding to drug delivery vectors and bactericides. An ➤

feel, enabling companies to estimate the reaction to new formulations – before they are subject to consumer testing.

The bottom line

Real-time, reliable image analysis of product quality identifies factors that lead to inferior product, well before it is too late to fix them. It also ensures more effective and consistent use of consumer preference models, likely resulting in lower costs and faster time to market. The Accelrys Platform also facilitates real-time integration with current quality control systems, minimising costly errors and unhappy customers.

This same analysis can, of course, be

applied to any QA process that requires analysis of unstructured data. This includes, for example, the visual screening of liquids for clarity; or the analysis of analytical instrument data to detect contaminants.

SCIENTIFIC COMPUTING WORLD APRIL/MAY 2010

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