摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员讨论机器学习的新发现。根据新闻报道由NewsRx记者发源于中华人民共和国广州，研究称，MAPbI3-based钙钛矿太阳电池(PSCs)有效钝化材料的快速鉴定仍然是一个巨大的挑战，我们利用机器学习的力量来辨别复杂的相关性钝化材料分子特性与功率转换效率(PCE)私营保安公司。结果表明，分子结构的复杂性、分子量、O原子、分子量、在MAPbI3-based PSC中，氢键受体与PCE相关。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingoriginating from Guangzhou, People ’s Republic of China, by NewsRx correspondents, research stated,“The swift iden tification of effective passivation materials for MAPbI3-based perovskite solar cells (PSCs)remains a formidable challenge, we employ the power of machine lear ning to discern the complex correlationbetween the molecular characteristics of passivation materials and the power conversion efficiency (PCE)of PSCs. Our re sults show that molecular characteristics such as complexity, molecular weight, O atoms,and hydrogen bond receptors are associated with PCE in MAPbI3-based PSC s.”
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