Mon, May 12, 2025
[ Mon, May 12th ]: wjla
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[ Mon, May 12th ]: CBS News
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[ Mon, May 12th ]: Today
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[ Mon, May 12th ]: Chicago Tribune
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[ Mon, May 12th ]: The Boston Globe
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[ Mon, May 12th ]: NPR
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[ Mon, May 12th ]: WTOP News
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[ Mon, May 12th ]: BBC
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[ Mon, May 12th ]: The New Indian Express
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Sun, May 11, 2025
[ Sun, May 11th ]: SmartCompany
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[ Sun, May 11th ]: yahoo.com
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[ Sun, May 11th ]: Telangana Today
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[ Sun, May 11th ]: Ghanaweb.com
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[ Sun, May 11th ]: Daily Mail
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[ Sun, May 11th ]: MLive
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[ Sun, May 11th ]: The Oklahoman
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[ Sun, May 11th ]: Mid Day
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[ Sun, May 11th ]: Seeking Alpha
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Sat, May 10, 2025
[ Sat, May 10th ]: Fox 11 News
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[ Sat, May 10th ]: Phys.org
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[ Sat, May 10th ]: wjla
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[ Sat, May 10th ]: Prevention
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[ Sat, May 10th ]: BBC
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Fri, May 9, 2025
[ Fri, May 09th ]: Forbes
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[ Fri, May 09th ]: BBC
Here's why your hay fever is so bad right now

Machine learning approach leads to discovery of high-performance infrared functional materials

  Copy link into your clipboard //science-technology.news-articles.net/content/2 .. h-performance-infrared-functional-materials.html
  Print publication without navigation Published in Science and Technology on by Phys.org
          🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source
Infrared optoelectronic functional materials are essential for applications in lasers, photodetectors, and infrared imaging, forming the technological backbone of modern optoelectronics. Traditionally, the development of new infrared materials has relied heavily on trial-and-error experimental methods. However, these approaches can be inefficient within the extensive chemical landscape, as only a limited number of compounds can achieve a balance of several critical properties simultaneously.
The article from phys.org, dated May 2025, discusses a groundbreaking machine learning approach developed by researchers to discover new materials with high infrared absorption. This method has led to the identification of several promising compounds that could significantly enhance the performance of infrared detectors and thermal imaging systems. By leveraging vast datasets and advanced algorithms, the team was able to predict and validate materials that exhibit superior infrared absorption properties, potentially revolutionizing applications in security, medical diagnostics, and environmental monitoring. The success of this approach underscores the power of machine learning in accelerating materials discovery and advancing technological innovations.

Read the Full Phys.org Article at:
[ https://phys.org/news/2025-05-machine-approach-discovery-high-infrared.html ]