Autoregressive neural-network wavefunctions for ab initio quantum chemistry
Autoregressive neural-network wavefunctions for ab initio quantum chemistry
复制标题
从头算量子化学的自回归神经网络波函数
DOI：
10.1038/s42256-022-00461-z
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发表时间：
2021
影响因子：
23.8
通讯作者：
A. Lvovsky
中科院分区：
文献类型：
--
作者：
T. Barrett;A. Malyshev;A. Lvovsky
近年来，神经网络量子态已经成为研究量子多体系统的有力工具。电子结构计算就是这样一个典型的多体问题，它已经吸引了几十年的持续研究，而最近才开始尝试神经网络量子态。然而，复杂的非局部相互作用和高样本复杂性是需要定制解决方案的重大挑战。在这里，我们参数化的电子波函数与自回归神经网络，允许高效和可扩展的采样，同时还嵌入物理先验反映分子系统的结构，而不牺牲可表达性。这使我们能够进行电子结构计算的分子与多达30个自旋轨道至少一个数量级以上的斯莱特决定因素比以前的应用程序的传统神经网络量子态，我们发现，我们的anterior可以胜过事实上的金标准耦合集群方法，即使在存在强量子相关性。对于一个高度表达的神经网络，采样不再是一个计算瓶颈，我们得出结论，进一步缩放的障碍与波函数本身无关，而是任何变分蒙特卡罗方法所固有的。为了在量子化学系统中进行电子结构计算，需要随着感兴趣的分子的大小增加而精确且可扩展的方法。Barrett和他的同事使用了一种自回归神经网络模型，使他们能够研究比以前使用神经网络量子态方法尝试的更大的分子。
In recent years, neural-network quantum states have emerged as powerful tools for the study of quantum many-body systems. Electronic structure calculations are one such canonical many-body problem that have attracted sustained research efforts spanning multiple decades, whilst only recently being attempted with neural-network quantum states. However, the complex non-local interactions and high sample complexity are substantial challenges that call for bespoke solutions. Here, we parameterize the electronic wavefunction with an autoregressive neural network that permits highly efficient and scalable sampling, whilst also embedding physical priors reflecting the structure of molecular systems without sacrificing expressibility. This allows us to perform electronic structure calculations on molecules with up to 30 spin orbitals—at least an order of magnitude more Slater determinants than previous applications of conventional neural-network quantum states—and we find that our ansatz can outperform the de facto gold-standard coupled-cluster methods even in the presence of strong quantum correlations. With a highly expressive neural network for which sampling is no longer a computational bottleneck, we conclude that the barriers to further scaling are not associated with the wavefunction ansatz itself, but rather are inherent to any variational Monte Carlo approach. To perform electronic structure calculations in quantum chemistry systems, methods are needed that are both accurate and scalable as the size of the molecule of interest increases. Barrett and colleagues employ an autoregressive neural-network ansatz that allows them to study larger molecules than previously attempted with neural-network quantum state approaches.
影响因子：
6.7
作者：
McClean, Jarrod R.;Rubin, Nicholas C.;Babbush, Ryan
通讯作者：
Babbush, Ryan