首页|Nanyang Technological University Reports Findings in Machine Learning (A machine learning-based framework for mapping hydrogen at the atomic scale)
Nanyang Technological University Reports Findings in Machine Learning (A machine learning-based framework for mapping hydrogen at the atomic scale)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Singapore, Singapore, by NewsRx correspondents, research stated, “Hydrogen, the lightest and most abun dant element in the universe, plays essential roles in a variety of clean energy technologies and industrial processes. For over a century, it has been known th at hydrogen can significantly degrade the mechanical properties of materials, le ading to issues like hydrogen embrittlement.” Our news journalists obtained a quote from the research from Nanyang Technologic al University, “A major challenge that has significantly limited scientific adva nces in this field is that light atoms like hydrogen are difficult to image, eve n with state-of-the-art microscopic techniques. To address this challenge, here, we introduce Atom-H, a versatile and generalizable machine learning-based frame work for imaging hydrogen atoms at the atomic scale. Using a high-resolution ele ctron microscope image as input, Atom-H accurately captures the distribution of hydrogen atoms and local stresses at lattice defects, including dislocations, gr ain boundaries, cracks, and phase boundaries. This provides atomic-scale insight s into hydrogen-governed mechanical behaviors in metallic materials, including p ure metals like Ni, Fe, Ti and alloys like FeCr.”
SingaporeSingaporeAsiaCyborgsEle mentsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine Lear ning
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