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Press Release | Argonne National Laboratory

Five Argonne technologies have been named finalists in the 2018 R&D 100 Awards

Five Argonne National Laboratory entries to the R&D 100 Awards, long considered the Oscars” of scientific innovation, have been named finalists.

The elite competition, sponsored by R&D Magazine, recognizes the 100 most innovative technologies of the previous year. Winning projects have included sophisticated testing equipment, innovative new materials, disruptive chemistry breakthroughs, new biomedical products, breakthrough consumer products and new technologies spanning industry, academia and government.

A total of 130 R&100 Awards have gone to Argonne scientists since the competition began in 1964.

This year’s entries include:

Automated Assignment of Rotational Spectra Using Artificial Neural Networks (RAINet)

Kirill Prozument and Daniel Zaleski

RAINet is an artificial neural network that automatically identifies physical parameters of molecules in the gas phase by their rotational spectra. Compared to mass spectrometry, rotational spectroscopy conveys richer information on chemical species. RAINet enables rotational spectroscopy by streamlining conversion of spectral information to chemical composition.

Kirill Prozument of the RAINet team. Not pictured from the RAINet team is Daniel Zaleski. (Image by Argonne National Laboratory.)

 

HyMag Magnets

Kaizhong Gao and Yuepeng Zhang

Their submittal proposes a revolutionary technology (HyMag) that significantly increases the usable magnetic flux density of a permanent magnet by 10%-30%, leading to a dramatic improvement in energy efficiency of electric motors and wind turbine generators. HyMag are less expensive and more environmentally friendly, with 60-90% lower heavy-rare-earth materials consumption.

(From left) Yuepeng Zhang and Kaizhong Gao of the HyMag Magnets team. (Image by Argonne National Laboratory.)

 

Darshan Software

Phil Carns, Kevin Harms, Robert Latham, Shane Snyder and Robert Ross

Darshan is a software product used to understand and improve the performance of the world’s largest data-intensive computing applications in fields such as physics, cosmology, chemistry, biology, aerospace and earth science. It has become the de facto standard for optimizing scientific data access in government, academic and industry computing centers around the world.

(From left) Kevin Harms, Shane Snyder, Philip Carns, Robert Ross and Robert Latham of the Darshan team. (Image by Argonne National Laboratory.)

 

The Grassroots Infrastructure Dependency Model (GRID-M)

Providing Public Safety Officials with Near-Real-Time Status of Critical Supply Chains following Major Disasters
Kyle Pfeiffer, Tom Wall, Carmella Burdi and Scott Schlueter

GRID-M is offered at no charge to the Federal Emergency Management Agency (FEMA), which sponsored its development, and will be free to all Federal sponsors. It enables near-real-time analysis of the physical infrastructure dependencies of supply and demand nodes (e.g., grocery store, utility substation) within a given jurisdiction following a disruptive event, such as an operational contingency (e.g., grid blackout) or natural hazards — the two most common types — or disruptions arising from terrorism or political instability. By scaling the complex supply chain processes to a manageable paradigm, GRID-M reduces the otherwise daunting computational load associated with providing such information in real time. It then analyzes recovery and restoration of service by tracking data in six lifeline infrastructure systems — electricity, natural gas, steam, telecommunications, water and wastewater — within a given supply chain. Emergency planners/managers and public safety officers can customize the tool to consider the critical infrastructure, represented as supply and demand nodes, of the supply chains of greatest significance within their jurisdictions.

(From left) Scott Schlueter, Carmella Burdi and Tom Wall of the GRID-M team. Not pictured from the GRID-M team is Kyle Pfeiffer. (Image by Argonne National Laboratory.)

 

Swift/T

Dataflow Programming for Scientific Supercomputing Workflows
Justin Wozniak, Jonathan Ozik, Nicholson Collier, Michael Wilde and Ian Foster in addition to two outside collaborators

Swift/T is a highly scalable, hierarchical parallel programming language and runtime that automatically parallelizes the execution of highly concurrent ensembles of scientific simulations on supercomputers.

Justin Wozniak of the Swift/T team. Not pictured from the Swift/T team are Jonathan Ozik, Nicholson Collier, Ian Foster, Michael Wilde, Tim Armstrong, and Dan Katz. (Image by Argonne National Laboratory.)

 

The R&100 Awards span five categories: Analytical/Test, IT/Electrical, Mechanical Devices/Materials, Process/Prototyping and Software/Services. For the third year, the R&100 Awards Committee will also honor excellence with four Special Recognition Awards:  Market Disruptor Services, Market Disruptor Products, Corporate Social Responsibility and Green Tech.

The competition’s 180 finalists were selected by an independent panel of more than 50 judges representing R&D leaders in a variety of fields. Winners will be announced November 16 at the 2018 R&100 Awards and Technology Conference in Orlando.