Consultancy : Research : National
Argonne is a multidisciplinary science and engineering research center, where talented scientists and engineers work together to answer the biggest questions facing humanity, from how to obtain affordable clean energy to protecting ourselves and our environment. Ever since we were born out of the University of Chicago’s work on the Manhattan Project in the 1940s, our goal has been to make an impact — from the atomic to the human to the global scale. The laboratory works in concert with universities, industry, and other national laboratories on questions and experiments too large for any one institution to do by itself. Through collaborations here and around the world, we strive to discover new ways to develop energy innovations through science, create novel materials molecule-by-molecule, and gain a deeper understanding of our planet, our climate, and the cosmos. Surrounded by the highest concentration of top-tier research organizations in the world, Argonne leverages its Chicago-area location to lead discovery and to power innovation in a wide range of core scientific capabilities, from high-energy physics and materials science to biology and advanced computer science.
Machine-Learning-Enhanced Simulation Could Reduce Energy Costs in Materials Production
Thanks to a new computational effort being pioneered by the U.S. Department of Energy’s (DOE) Argonne National Laboratory in conjunction with 3M and supported by the DOE’S High Performance Computing for Energy Innovation (HPC4EI) program, researchers are finding new ways to dramatically reduce the amount of energy required for melt blowing the materials needed in N95 masks and other applications.
Currently, the process used to create a nozzle to spin nonwoven materials produces a very high-quality product, but it is quite energy intensive. Approximately 300,000 tons of melt-blown materials are produced annually worldwide, requiring roughly 245 gigawatt-hours per year of energy, approximately the amount generated by a large solar farm. By using Argonne supercomputing resources to pair computational fluid dynamics simulations and machine-learning techniques, the Argonne and 3M collaboration sought to reduce energy consumption by 20% without compromising material quality.
Because the process of making a new nozzle is very expensive, the information gained from the machine-learning model can equip material manufacturers with a way to narrow down to a set of optimal designs. ”Machine-learning-enhanced simulation is the best way of cheaply getting at the right combination of parameters like temperatures, material composition, and pressures for creating these materials at high quality with less energy,” Blaiszik said.
A New Way to Discover a Reaction that Causes Cracks in Concrete
One phenomenon that shortens the life of concrete buildings and structures is the alkali-silica reaction (ASR). It is the reaction between alkali ions found in cement and silica, the two main components of concrete, which creates a gel that absorbs water and expands, causing internal pressures to build up within the concrete. To help identify the extent of ASR, researchers at the Argonne National Laboratory have discovered a harmless way to detect it that could reduce the level of expensive testing being done. Their new method relies on electrochemical impedance spectroscopy (EIS), which measures electrical conductivity.
Scientists Set to Use Social Media AI Technology to Optimize Parts for 3D Printing
“My idea was that a material’s structure is no different than a 3D image,” he explains. “It makes sense that the 3D version of this neural network will do a good job of recognizing the structure’s properties — just like a neural network learns that an image is a cat or something else.”
To see if his idea would work, Messner designed a defined 3D geometry and used conventional physics-based simulations to create a set of two million data points. Each of the data points linked his geometry to ‘desired’ values of density and stiffness. Then, he fed the data points into a neural network and trained it to look for the desired properties.
Finally, Messner used a genetic algorithm – an iterative, optimization-based class of AI – together with the trained neural network to determine the structure that would result in the properties he sought. Impressively, his AI approach found the correct structure 2,760x faster than the conventional physics simulation.
Leveraging AI and Statistical Methods to Improve Flame Spray Pyrolysis
Flame spray pyrolysis has long been used to make small particles that can be used as paint pigments. Now, researchers at Argonne National Laboratory are refining the process to make smaller, nano-sized particles of various materials that can make nano-powders for low-cobalt battery cathodes, solid state electrolytes and platinum/titanium dioxide catalysts for turning biomass into fuel.