论文标题
机器学习波函数
Machine Learning Wavefunction
论文作者
论文摘要
本章介绍了通过机器学习模型来表示电子波函数的主要思想和最重要的方法。 N-电子系统的波函数是一个非常复杂的数学对象，其模型需要足够的灵活性来正确描述粒子之间的复杂相互作用，但同时却是足够紧凑的表示在实践中有用的。机器学习技术提供了一个理想的数学框架来满足这些要求，并为其在受监督和无监督的时尚中的优化提供算法。在本章中，提出了机器学习波函数的各种示例，并讨论了它们相对于传统量子化学方法的优势和缺点。从理论上讲，在实践中首先进行两个案例研究。
This chapter introduces the main ideas and the most important methods for representing the electronic wavefunction through machine learning models. The wavefunction of a N-electron system is an incredibly complicated mathematical object, and models thereof require enough flexibility to properly describe the complex interactions between the particles, but at the same time a sufficiently compact representation to be useful in practice. Machine learning techniques offer an ideal mathematical framework to satisfy these requirements, and provide algorithms for their optimization in both supervised and unsupervised fashions. In this chapter, various examples of machine learning wavefunctions are presented and their strengths and weaknesses with respect to traditional quantum chemical approaches are discussed; first in theory, and then in practice with two case studies.
