摘要
由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-关于机器学习的详细数据已经呈现。根据来自德国柏林的新闻报道，By NewsRx编辑，这项研究指出，“计算异质催化的未来是由机器学习在两个不同但同样重要的领域塑造的：(i)原子势的发展，这种势与DFT和基于波函数的从头算方法(MP2，CCSD(T)非常接近，但在计算上效率较高，以及(ii)寻找用于预测催化材料和反应的结构和反应性描述符。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating from Berlin, Germany, b y NewsRx editors, the research stated, “The future of computational heterogeneou s catalysis is shaped by machine learning in two different but equally important areas: (i) development of atomistic potentials that closely approximate DFT and wavefunction based ab initio methods (MP2, CCSD(T)), but are computationally mo re efficient, and (ii) finding structure and reactivity descriptors for predicti ng catalytic materials and reactions.”
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