摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsRx记者从宾夕法尼亚州匹兹堡发回的新闻报道，研究人员称：“对绿色氢氧化物的需求引起了人们对用于氧气净化反应催化剂的铱可用性的担忧。我们通过机器学习辅助计算管道来识别催化剂，该管道训练了超过36000种混合金属氧化物。”我们的新闻编辑从卡内基梅隆大学的研究中获得了一句话：“该管道精确地预测了UNR Elaxed结构的Pourbaix分解能量()，平均绝对误差为每原子77MeV，使我们能够筛选2070种新的金属氧化物在酸性条件下的稳定性。该搜索确定RuCrTiO是一种具有提高耐久性优点的候选材料：实验，我们发现它在100mAcm下提供267mV的过电位，并且在该电流密度下工作超过200h，显示出25mVh的过电位增长速率。表面密度泛函理论计算表明，Ti增加了金属-氧共价Ncy，这是提高稳定性的潜在途径，而Cr降低了HO*形成速率决定步骤的能垒R，"与RuO相比活性增加,在100 mA cm时过电位降低40 mV,同时保持稳定性."
Abstract
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 from Pittsburgh, Pennsylvania, by NewsRx correspondents, research stated, "The demand for green h ydrogen has raised concerns over the availability of iridium used in oxygen evol ution reaction catalysts. We identify catalysts with the aid of a machine learni ng-aided computational pipeline trained on more than 36,000 mixed metal oxides." Our news editors obtained a quote from the research from Carnegie Mellon Univers ity, "The pipeline accurately predicts Pourbaix decomposition energy () from unr elaxed structures with a mean absolute error of 77 meV per atom, enabling us to screen 2070 new metallic oxides with respect to their prospective stability unde r acidic conditions. The search identifies RuCrTiO as a candidate having the pro mise of increased durability: experimentally, we find that it provides an overpo tential of 267 mV at 100 mA cm and that it operates at this current density for over 200 h and exhibits a rate of overpotential increase of 25 mV h. Surface den sity functional theory calculations reveal that Ti increases metal-oxygen covale ncy, a potential route to increased stability, while Cr lowers the energy barrie r of the HOO* formation rate-determining step, increasing activity compared to R uO and reducing overpotential by 40 mV at 100 mA cm while maintaining stability. "
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