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DOE/Argonne National Laboratory


Scaling forward

A researcher at Argonne National Laboratory has developed a faster way to create molecular models, accelerating the screening of potential new organic materials for electronics. The approach uses machine learning to predict electronic properties and enables scientists to screen more packing arrangements than before.

SourceDOE/Argonne National Laboratory·JournalScience Advances·DateMar 22, 2019

New tools in transportation

The AFLEET Tool and its online version enable fleet managers to compare the costs and benefits of alternative fuels and vehicle technologies, optimizing their purchasing decisions. With over 8,000 users, the tool is based on Argonne's GREET model and provides a comprehensive and easier way to make informed decisions.

Blast to the future

Researchers at Argonne National Laboratory are developing a machine learning-based framework called BLAST to accelerate and simplify materials modeling and simulation. This software will enable companies to quickly perform molecular dynamics simulations needed for new material vetting, with applications in polymers and steel alloys.

Greater than the sum of its parts

Scientists have developed a comprehensive model of electrochemistry that combines existing theories to predict previously unexplained behavior. The Unified Electrochemical Band-Diagram Framework enables the prediction of material properties and behavior in any electrode, including batteries, supercapacitors, and catalysis.

SourceDOE/Argonne National Laboratory·JournalAdvanced Functional Materials·DateSep 18, 2018

A trick of the light

Researchers at Argonne National Laboratory developed nanoparticle coatings that increase the sensitivity of photodetectors to UV radiation, enabling the detection of rare events and potential insights into neutrino oscillations. These enhanced detectors could also be used to enhance visible light in dim environments.

SourceDOE/Argonne National Laboratory·JournalScientific Reports·DateSep 11, 2018

Relax, just break it

Researchers at Argonne National Laboratory used novel tools to study local order in relaxor ferroelectrics, revealing a correlation between butterfly-shaped diffuse scattering and piezoelectric behavior. This discovery could lead to the development of non-lead-based materials with improved properties.

SourceDOE/Argonne National Laboratory·JournalNature Materials·DateJul 19, 2018