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Using machine learning to build a better battery - Innovation Toronto

We believe that machine learning represents a way to get a molecular picture that is nearly as precise at a fraction of the computational cost ....

Ian Foster , Rajeev Assary , Stanford University , Us Department Of Energy , Argonne National Laboratory , Argonne Data Science , Logan Ward ,

Machine learning model speeds up assessing catalysts for decarbonization technology from months to milliseconds

Next time you drive past farms or prairies and ponds on a rural road, look around. They are a rich source of biomass. That includes corn, soybeans, sugar cane, switchgrass, algae and other plant m . ....

United States , Hieu Doan , Rajeev Assary , Chemical Catalysis For Bioenergy Consortium , Office Of Science , Argonne National Laboratory , Argonne Materials Science Division , Us Department Of Energy , Materials Science Division , Argonne Leadership Computing Facility , Chemical Catalysis , Machine Learning , Computer Simulations , Deep Learning , Artificial Intelligence ,

Machine Learning Model Accelerates Assessment of Catalysts for Biomass Conversion to Useful Products

Scientists create computational model for identifying low-cost catalysts that convert biomass into fuels and useful chemicals with low carbon footprint. ....

United States , Larry Curtiss , Chenyang Li , Hieu Doan , Laura Thomsonmar , Rajeev Assary , Mingxia Zhou , Royal Society Of Chemistry , Argonne Materials Science Division , Us Department Of Energy , Renewable Energy , Chemical Catalysis For Bioenergy Consortium , Office Of Science , Argonne National Laboratory , Consortium For Computational Physics , Office Of Energy Efficiency , Materials Science Division , Argonne Leadership Computing Facility , Chemical Catalysis , Digital Discovery , Royal Society , Logan Ward , Energy Efficiency , Computational Physics ,

Using AI for Optimization of Several Flow Battery Properties Simultaneously

Scientists seek stable, high-energy batteries designed for the electric grid. ....

Lily Robertson , Lu Zhang , Garvit Agarwal , Laura Thomsonnov , Hieu Doan , Rajeev Assary , Argonne Joint Center , Energy Storage Research , Argonne Laboratory Computing Resource Center , Us Department Of Energy , Office Of Basic Energy Sciences , Office Of Science , Argonne National Laboratory , Energy Innovation Hub , Joint Center , Basic Energy , Laboratory Computing Resource Center , Nanoscale Materials ,

ORNL: Novel composite catalyst holds promise for cost-effective ethanol-to-jet-fuel conversion

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Decarbonizing air transportation is critical to meeting U.S. climate goals and improving the nation’s energy economy. But technologies that are transforming automobiles – such as electric motors and hydrogen fuels – are difficult to implement in aircraft.
A battery powerful enough to fuel an airplane would be prohibitively heavy. Hydrogen is only one quarter as energy dense as jet fuel (and many times more expensive) but would require large complex storage tanks onboard. To greatly reduce its emissions, the U.S. commercial aviation sector will need new methods of making sustainable aviation fuel.
The well-established, cost-competitive ethanol market provides an opportunity to shift the composition of jet fuel and other fuel products away from petroleum. The Department of Energy’s Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office is focused on developing industrially viable fuels using renewable biomass, including national labo ....

Junyan Zhang , Jeremy Kropf , Zhenglong Li , Rajeev Assary , Theodore Krause , Lawrence Allard , Stephen Purdy , Dongxia Liu , Huamin Wang , Kinga Unocic , Jeffrey Miller , Mingxia Zhou , Purdue University , University Of Maryland , Pacific Northwest National Laboratory , Renewable Energy Bioenergy Technologies Office , Oak Ridge National Laboratory , Argonne National Laboratory , Department Of Energy Office Efficiency , Energy Efficiency , Ridge National Laboratory , Even Wegener , Fan Lin , ஜெர்மி க்ரோபப் , ஸ்டீபன் ஊதா , ஜெஃப்ரி மில்லர் ,