摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者在福建的新闻报道，研究表明：“由于避免了辅助的单体轨道，无轨道密度泛函理论(DFT)比依赖轨道的Kohn-Sham DF T效率高得多，机器学习方法最近被应用于无轨道DFT的构建[Wu等，Phys.Rev.”
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 from Fujian, People’s Republi c of China, by NewsRx journalists, research stated, “Orbital-free density functi onal theory (DFT) is much more efficient than the orbital-dependent Kohn-Sham DF T due to the avoidance of the auxiliary one-body orbitals. The machine learning approach has been applied to build nuclear orbital-free DFT recently [Wu et al., Phys. Rev.”
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