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Engineers Apply Physics-Informed Machine Learning to Solar Cell Production Using Frontera

June 9, 2021 Today, solar energy provides 2% of U.S. power. However, by 2050, renewables are predicted to be the most used energy source (surpassing ....

United States , Joydeep Munshi , Ganesh Balasubramanian , Lehigh University , Texas Advanced Computing Center , Mechanical Engineering , Chemical Information , ஒன்றுபட்டது மாநிலங்களில் , ஜோய்தீப் முன்ஷி , கணேஷ் பாலசுப்ரமணியன் , லேஹி பல்கலைக்கழகம் , இயந்திர பொறியியல் , இரசாயன தகவல் ,

Engineers apply physics-informed machine learning to solar cell production

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IMAGE: Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field..
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Credit: Department of Mechanical Engineering and Mechanics/Lehigh University
Today, solar energy provides 2% of U.S. power. However, by 2050, renewables are predicted to be the most used energy source (surpassing petroleum and other liquids, natural gas, and coal) and solar will overtake wind as the leading source of renewable power. To reach that point, and to make solar power more affordable, solar technologies still require a number of breakthroughs. One is the ability to more efficiently transform photons of light from the Sun into useable energy. ....

United States , Joydeep Munshi , Ganesh Balasubramanian , Lehigh University , Texas Advanced Computing Center , Department Of Mechanical Engineering , Wei Chen Northwestern University , Mechanics Lehigh University , Mechanical Engineering , Computational Materials Science , Wei Chen , Northwestern University , Teyu Chien , Cuckoo Search Coarse Grained Molecular Dynamics , Chemical Information , Chemistry Physics Materials Sciences , Industrial Engineering Chemistry , Polymer Chemistry , Energy Sources , Algorithms Models , Biomedical Environmental Chemical Engineering , Computer Science , Software Engineering , ஒன்றுபட்டது மாநிலங்களில் , ஜோய்தீப் முன்ஷி , கணேஷ் பாலசுப்ரமணியன் ,

Borrowing from birds, experts reduce search times for novel high-entropy alloys to seconds

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IMAGE: Computational materials scientists at Ames Laboratory developed an evolutionary algorithm, using a hybrid version of a computer program called Cuckoo Search (CS), which mimics the brood parasite behavior of cuckoo.
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Credit: U.S. Department of Energy, Ames Laboratory
Computational materials science experts at the U.S. Department of Energy s Ames Laboratory enhanced an algorithm that borrows its approach from the nesting habits of cuckoo birds, reducing the search time for new high-tech alloys from weeks to mere seconds.
The scientists are investigating a type of alloys called high-entropy alloys, a novel class of materials that are highly sought after for a host of unusual and potentially beneficial properties. They are lightweight in relation to their strength, fracture-resistant, highly corrosion and oxidation resistant, and stand up well in high-temperature and high-pressure environments making them attractive materials fo ....

United States , Duane Johnson , Rahul Singh , Monte Carlo , Prashant Singh , Duaned Johnson , Aayush Sharma , Ganesh Balasubramanian , Us Department Of Energy Ames Laboratory , Ames Laboratory , Office Of Science , Iowa State University , Games Lab , Us Department Of Energy , Cuckoo Search , Nature Computational , Energy Office , Science National Laboratory , Iowa State , Chemistry Physics Materials Sciences , Algorithms Models , Theory Design , ஒன்றுபட்டது மாநிலங்களில் , ட்வேந் ஜான்சன் , ராகுல் சிங் , மான்டே கார்லோ ,

Experts reduce search times for novel high-entropy alloys 13,000-fold using Cuckoo Search

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IMAGE: A major roadblock to computational design of high-entropy alloys has been removed, according to scientists at Iowa State University and Lehigh University. Engineers from the Ames Lab and Lehigh University s.
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Credit: Ames Laboratory, U.S. Department of Energy
A major roadblock to computational design of high-entropy alloys has been removed, according to scientists at Iowa State University and Lehigh University. Engineers from the Ames Lab and Lehigh University s Department of Mechanical Engineering and Mechanics have developed a process that reduces search time used for predictive design 13,000-fold.
According to Ganesh Balasubramanian, an associate professor at Lehigh, the goal of the team s research was to accelerate the computational modeling of complex alloys. The tools available for creating random distribution of atoms in materials simulation models, he says, have been used for many, many years now and are limited in the ....

Rahul Singh , Prashant Singh , Ganesh Balasubramanian , Aayush Sharma , Duaned Johnson , Lehigh University , Ames Laboratory , Lehigh University Department Of Mechanical Engineering , Iowa State University , National Science Foundation , Games Lab , Mechanical Engineering , Cuckoo Search , Nature Computational Science , Energy Office , Science National Laboratory , Iowa State , Chemistry Physics Materials Sciences , Algorithms Models , Technology Engineering Computer Science , Computer Science , Research Development , Robotry Artificial Intelligence , ராகுல் சிங் , பிரஷண்ட் சிங் , கணேஷ் பாலசுப்ரமணியன் ,