首页|Dalian University of Technology Reports Findings in Machine Learning (Key Factor s of Uniform Polarization Reversal Barrier in Wurtzite Materials Utilizing Machi ne Learning Methods)
Dalian University of Technology Reports Findings in Machine Learning (Key Factor s of Uniform Polarization Reversal Barrier in Wurtzite Materials Utilizing Machi ne Learning Methods)
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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 out of Dalian, People’s Repub lic of China, by NewsRx editors, research stated, “Scandium-doped aluminum nitri de with a wurtzite structure has emerged as a promising ferroelectric material d ue to its exceptional physical and chemical properties and its compatibility wit h existing processing techniques. However, its high coercive voltage presents a substantial challenge for its potential applications.” Our news journalists obtained a quote from the research from the Dalian Universi ty of Technology, “To effectively reduce this high coercive voltage, it is cruci al to comprehensively understand the factors governing polarization reversal pro cesses. Unfortunately, a unified set of pivotal factors has not yet been identif ied. Herein, machine-learning regression models were developed to predict the un iform polarization reversal barrier () using data sets comprising 41 binary and 113 simple ternary wurtzite materials. Features were extracted based on elementa l properties, crystal parameters, mechanical properties, and electronic properti es. Calculation of and partial feature extraction were performed using first-pri nciples methods. The results revealed that the average cation-ion potential is t he primary intrinsic factor influencing. Additionally, the maximum value of the relative height ratio of cations to anions, cell parameter ratio, and average ca tion Mendeleev number were found to have secondary impacts. This study addresses gaps in the current understanding of , by considering multiple influencing fact ors beyond a single material system.”
DalianPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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