A research team led by Dr. Jeong Min Park of the Nano Materials Research Division at the Korea Institute of Materials Science (KIMS), in collaboration with Dr. Jaemin Wang and Prof. Dierk Raabe of the Max Planck Institute in Germany, has developed an artificial intelligence (AI)-based model capable of assessing the likelihood and characteristics of internal defects during process design. This research achievement is expected to significantly enhance the reliability of metal additive manufacturing parts and greatly expand their applicability for mass production in industrial settings.

A new review highlights how machine learning is transforming the way scientists detect and measure organic pollutants in the environment, offering powerful new tools to overcome long standing analytical challenges.

A new international study finds that artificial intelligence development is increasingly splitting into three distinct global systems led by the United States, China, and the European Union, each shaped by different policy priorities, innovation models, and governance philosophies. The research suggests that this divergence may permanently reshape the technological landscape and complicate global cooperation on AI safety, standards, and innovation.

A newly launched scientific journal is calling for a deeper integration of artificial intelligence into environmental research, arguing that AI may be one of the most powerful tools available to address climate change, pollution, and resource sustainability.

When people expect that a beverage contains sugar, this expectation increases their enjoyment of beverages that only contain artificial sweeteners.

Steven Weisberg, a researcher at The University of Texas at Arlington, found that advanced artificial intelligence tools could not uncover a clear link between brain structure and navigation ability in healthy young adults—challenging long-standing ideas about how the brain helps us find our way.