标题： 机器能学习密度泛函吗？ DFT中机器学习的过去、现在和未来 显示英文标题

标题： Can machines learn density functionals? Past, present, and future of ML in DFT

摘要： 密度泛函理论已成为世界上最受欢迎的电子结构方法，并被常规应用于材料和分子。 在这里，我们回顾了最近尝试使用现代机器学习来改进密度泛函近似的方法。 许多不同的研究人员尝试了不同的方法，但一些共同的主题和经验已经浮现。 我们讨论这些趋势以及它们可能将我们带向的未来。 显示英文摘要

摘要： Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have emerged. We discuss these trends and where they might bring us in the future.