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TRƯỜNG ĐẠI HỌC KHOA HỌC TỰ NHIÊN, ĐẠI HỌC QUỐC GIA THÀNH PHỐ HỒ CHÍ MINH

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A team of scientists at Freie Universität Berlin has developed an Artificial Intelligence (AI) method that provides a fundamentally new solution of the "sampling problem" in statistical physics. The sampling problem is that important properties of materials and molecules can practically not be computed by directly simulating the motion of atoms in the computer because the required computational capacities are too vast even for supercomputers. The team developed a deep learning method that speeds up these calculations massively, making them feasible for previously intractable applications. "AI is changing all areas of our life, including the way we do science," explains Dr. Frank Noé, professor at Freie Universität Berlin and main author of the study. Several years ago, so-called deep learning methods bested human experts in pattern recognition—be it the reading of handwritten texts or the recognition of cancer cells from medical images. "Since these breakthroughs, AI research has skyrocketed. Every day, we see new developments in application areas where traditional methods have left us stuck for years. We believe our approach could be such an advance for the field of statistical physics." The results were published in Science.

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The three classic physical states—solid, liquid and gaseous—can be observed in any normal kitchen, for example when you bring an ice cube to the boil. But if you heat material even further, so that the atoms of a substance collide and the electrons separate from them, then another state is reached: plasma. More than 99 percent of material in space is present in this form, inside stars for instance. It is therefore no wonder that physicists are keen to study such material. Unfortunately, creating and studying plasmas on Earth using the high temperature and pressure that exist inside stars is extremely challenging for various reasons. Physicists at Friedrich Schiller University in Jena have now managed to solve some of these problems, and they have reported on their results in the renowned research journal Physical Review X.

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University of Chicago scientists are part of an international research team that has discovered superconductivity—the ability to conduct electricity perfectly—at the highest temperatures ever recorded.

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Could a small ringlike structure made of plastic and copper amplify the already powerful imaging capabilities of a magnetic resonance imaging (MRI) machine? Xin Zhang, Stephan Anderson, and their team at the Boston University Photonics Center can clearly picture such a feat. With their combined expertise in engineering, materials science, and medical imaging, Zhang and Anderson, along with Guangwu Duan and Xiaoguang Zhao, designed a new magnetic metamaterial, reported in Communications Physics, that can improve MRI quality and cut scan time in half.

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A team of researchers affiliated with several institutions in Spain and the U.S. has announced that they have discovered a new property of light—self-torque. In their paper published in the journal Science, the group describes how they happened to spot the new property and possible uses for it.

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Scientists from RIKEN and the University of California San Diego, in collaboration with international partners have found a way to significantly reduce the amount of energy required by organic light emitting diodes (OLEDs). OLEDs have attracted attention as potential replacements for liquid crystal diodes, since they offer advantages such as being flexible, thin, and not requiring backlighting.

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