首页|University of Cambridge Reports Findings in Machine Learning (Machine-Learning P redictions of Critical Temperatures from Chemical Compositions of Superconductor s)
University of Cambridge Reports Findings in Machine Learning (Machine-Learning P redictions of Critical Temperatures from Chemical Compositions of Superconductor s)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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 Cambridge, United King dom, by NewsRx editors, research stated, "In the quest for advanced superconduct ing materials, the accurate prediction of critical temperatures () poses a formi dable chAllenge, largely due to the complex interdependencies between supercondu cting properties and the chemical and structural characteristics of a given mate rial. To address this chAllenges, we have developed a machine-learning framework that aims to elucidate these complicated and hitherto poorly understood structu re-property and property-property relationships."
NETL
NSTL
