摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据NewsRx记者从长沙发来的新闻报道，研究表明：“先进脱氧剂的设计是促进化学链蒸汽裂解制氢的关键，但脱氧剂的合成可能性复杂，材料开发周期长，本文以吉布斯自由能变化(Delta G)作为模型的输出，并建立了三个机器学习模型，即决策树、随机森林和梯度提升树算法，并对功能化脱氧剂筛选进行了优化。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “The design of adv anced deoxidizer is the key to promote hydrogen production from chemical looping steam splitting, however, the deoxidizer shows complicated possibility of compo sition, which results in long duration in material exploitation. In this study, Gibbs free energy change ( Delta G) is used as the output of the model, and three machine learning models, Decision Tree, Random Forest, and Gradient Boosting Tree algorithms, are established and optimized for functionalized deoxidizer scre ening.”
NETL
NSTL