论文标题
使用神经网络量子状态求解真实固体的准粒子带光谱
Solving Quasiparticle Band Spectra of Real Solids using Neural-Network Quantum States
论文作者
论文摘要
为固体系统建立预测性AB的初始方法是凝结物理学和计算材料科学的基本目标之一。中心挑战是如何紧凑地编码高度复杂的量子量子函数。在这里，我们证明了人工神经网络在机器学习的背景下以压倒性的表达闻名，是对扩展周期性材料进行第一原理计算的绝佳工具。我们表明，精确模拟了一个，二维和三维系统中实际固体中的基态能量，达到其化学精度。我们工作的重点是，可以使用旨在利用神经网络的低较低能量结构的计算技术来有效提取固态系统必不可少且特有的准粒子带光谱。这项工作为阐明固态系统中有趣而复杂的多体现象的途径开辟了道路。
Establishing a predictive ab initio method for solid systems is one of the fundamental goals in condensed matter physics and computational materials science. The central challenge is how to encode a highly-complex quantum-many-body wave function compactly. Here, we demonstrate that artificial neural networks, known for their overwhelming expressibility in the context of machine learning, are excellent tool for first-principles calculations of extended periodic materials. We show that the ground-state energies in real solids in one-, two-, and three-dimensional systems are simulated precisely, reaching their chemical accuracy. The highlight of our work is that the quasiparticle band spectra, which are both essential and peculiar to solid-state systems, can be efficiently extracted with a computational technique designed to exploit the low-lying energy structure from neural networks. This work opens up a path to elucidate the intriguing and complex many-body phenomena in solid-state systems.
