论文标题
绕过与机器学习的强量相关性的量子插入理论的计算瓶颈
Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning
论文作者
论文摘要
对密切相关的物质进行量子力学模拟的基本障碍是，借助当前可用的理论工具，足够准确的计算通常太贵了，无法实现。在这里，我们设计了一个将量子装置（QE）方法与机器学习相结合的计算框架。这使我们能够完全绕过QE算法的最高计算型组件，从而使其总成本与裸密度功能理论（DFT）相当。我们对一系列Actinide系统进行基准计算，我们的方法准确地描述了相关效应，从而通过计算成本的数量级降低。我们认为，通过产生大规模的训练数据，可以将我们的方法应用于任意化学计量和晶体结构的系统，为在凝结物理，化学和材料科学中实际上无限应用铺平了道路。
A cardinal obstacle to performing quantum-mechanical simulations of strongly-correlated matter is that, with the theoretical tools presently available, sufficiently-accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally-expensive components of QE algorithms, making their overall cost comparable to bare Density Functional Theory (DFT). We perform benchmark calculations of a series of actinide systems, where our method describes accurately the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry and materials science.
