首页|University of Wisconsin Madison Reports Findings in Machine Learning (Phase Tran sition in Silicon from Machine Learning Informed Metadynamics)
University of Wisconsin Madison Reports Findings in Machine Learning (Phase Tran sition in Silicon from Machine Learning Informed Metadynamics)
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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 reporting originating in Madison, Unite d States, by NewsRx journalists, research stated, “Investigating reconstructive phase transitions in large-sized systems requires a highly efficient computation al framework with computational cost proportional to the system size. Traditiona lly, widely used frameworks such as density functional theory (DFT) have been pr ohibitively expensive for extensive simulations on large systems that require lo ng-time scales.”The news reporters obtained a quote from the research from the University of Wis consin Madison, “To address this challenge, this study employed well-trained mac hine learning potential to simulate phase transitions in a large-size system. Th is work integrates the metadynamics simulation approach with machine learning po tential, specifically deep potential, to enhance computational efficiency and ac celerate the study of phase transition and consequent development of grains and dislocation defects in a system. The new method is demonstrated using the phase transitions of bulk silicon under high pressure.”
MadisonUnited StatesNorth and Centra l AmericaCyborgsEmerging TechnologiesMachine Learning
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