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Computing + Experimentation = Faster, Better Solutions
Posted on November 10th, 2015 by Regina Javier in New Materials & Applications
Supercomputing technologies and similar solutions have been fueling a rise in computing-based science and engineering for years. With the exponential increase of available research and technical data, the demand for solutions that take all that content and process it to add a layer of decision-support capabilities such as descriptive, semantic, comparative and predictive analytics is palpable in research-intensive markets. In theory, this movement brings much potential for innovation in fields like advanced materials development, where the cost of physical experiments would be so great that computational methods become necessary.
The challenges surrounding experimentation and simulation are particularly evident in chemical and materials sciences, especially for projects that drive environmental sustainability. For example, climate change is a huge problem, and many research efforts surrounding this space — such as projecting how climate change will progress or identifying the best materials to use in solutions for energy efficiency — are highlighting the growing importance of unifying experiments and simulations.
This process brings up interesting questions on how scientists and engineers have so far adapted and will need to adapt their workflows and mindsets. For instance, being able to connect physical experimentation and simulation.
A 2013 article published in the Stanford Encyclopedia of Philosophy explained that the lines between experimentation and computational models have been blurring for years. The initial emergence of supercomputing systems as a tool to drive scientific innovation after World War II began in fields like meteorology, where experimentation isn’t feasible, but has since spread to more lab-based disciplines.
Much of this spread, according to the news source, stemmed from philosophies initially developed by John von Neumann, who theorized that data-based computational simulations could replace experiments in entirety with the right models in place. Within this framework, scientists can use information that they know is accurate and change variable data to prove theories without requiring experimentation. According to the article, the similarities between certain experimentation models and data-based simulations can be so great that the difference is negligible – with both potentially serving as a valid form of proving a hypothesis.
No matter where one sits in the spectrum of opinions and experience regarding this topic, what’s undeniable is that the growing connection between computing and experimentation creates opportunities and potential urgency for scientists and engineers to be able to use simulations and physical experiments alongside one another. Doing so could be key to accelerating the pace at which organizations can bring ideas from theory into reality, and providing solutions for big challenges like improving sustainability and reducing the carbon footprint in manufacturing.
And it seems some visible research funding initiatives are in agreement. For instance, the Engineering and Physical Sciences Research Council (EPSRC) recently announced a project to fund materials research efforts that integrate computational and experimental efforts. The initiative stands among similar efforts in a growing move to drive the commercial benefits of research projects into markets at a rapid pace, providing grant funding to a project that will be led by Professor Matthew Rosseinsky of the University of Liverpool.
Rosseinsky explained that sustainable energy efforts will be a key component of the project:
“The controlled arrangement of atoms and molecules to create function is a grand scientific challenge. With the approaches we will develop, we aim to address problems such as how to create materials for sustainable energy production and storage such as safer new battery technologies or the efficient capture and utilisation of solar energy.”
The effort is aimed at sharing its findings across commercial segments to eliminate the boundaries that exist across different disciplines. The goal here is to break down traditional barriers to innovation so scientists can bring ideas through experimental phases and out into commercial venues to maximize their economic impact. In particular, the results of the initiative will be shared with a dedicated research organization that combines stakeholders from science and industry to ensure results are ready to impact the economy.
In my admittedly layman’s terms, I think of the benefit this way – if one can use physical experiments to create a solid working model, then supplement those experiments with data-based simulations for variable conditions, then one can complete the design and development process much quicker than would have been possible if they had to configure and complete physical processes around each variable.
Rosseinsky’s project is just one example of a growing movement to integrate computing and experimentation. Another example is the Materials Genome Initiative (MGI) that launched years ago, which is aimed at improving materials science through a combination of computing and experimentation that drives innovation through the best elements of each scientific methodology.
Source: https://www.whitehouse.gov/mgi, 2015
As a prominently featured quote from President Obama on the MGI website shows, this government-sponsored effort is focused on commercial, economic and societal growth as well:
“To help businesses discover, develop and deploy new materials twice as fast, we’re launching what we call the Materials Genome Initiative. The invention of silicon circuits and lithium-ion batteries made computers and iPads and iPods possible – but it took years to take those technologies from the drawing board to the marketplace. We can do it faster.”
With material and chemical sciences playing a key role in fueling innovation, and challenges like sustainability having created a sense of urgency in specific sectors, it would be interesting to see more of how combining computing and experimentation will prove critical in helping research organizations and R&D departments keep up and meet innovation challenges. I’ve cited some examples above and would be really interested to hear whether there are other examples in the market pointing to the same trend or the opposite of it. Care to share?
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