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.

Thomas D. Barrett

Aleksei Malyshev

A. I. Lvovsky

Nature Machine Intelligence

NATURE MACHINE INTELLIGENCE 将在机器学习、机器人和人工智能的广泛主题中发表高质量的原创研究和评论。该杂志还将探讨和讨论这些领域开始对其他科学学科以及社会和工业的许多方面产生的重大影响。在科学发现、医疗保健、医学诊断以及安全和可持续的城市、交通和农业等领域，机器智能可以增强人类能力和知识的机会不计其数。与此同时，出现了许多关于道德、社会和法律问题的重要问题，特别是考虑到快速发展的自然机器智能将提供一个平台来讨论这些广泛的影响——鼓励跨学科对话——通过评论、新闻专题、新闻和观点文章以及通信。

Nature Machine Intelligence will publish high-quality original research and reviews on a wide range of topics in machine learning, robotics, and artificial intelligence. The journal will also explore and discuss the significant impacts these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. The opportunities for machine intelligence to enhance human capabilities and knowledge are countless, particularly in areas such as scientific discovery, healthcare, medical diagnostics, and safe and sustainable cities, transportation, and agriculture. At the same time, many important ethical, social, and legal issues have emerged, especially considering the rapid development of natural machine intelligence. The journal will provide a platform to discuss these broad impacts, encouraging interdisciplinary dialogue through reviews, news features, news and opinion articles, and communications.

《Nature Machine Intelligence》将发表关于机器学习、机器人和人工智能等广泛主题的高质量原创研究和评论。该期刊还将探讨这些领域对其他科学学科、社会和工业的重大影响。在科学发现、医疗保健、医学诊断，以及安全和可持续的城市、交通和农业等领域，机器智能为增强人类能力和知识提供了无数机会。同时，随着自然机器智能的快速发展，许多关于伦理、社会和法律的问题也随之出现。该期刊将为讨论这些广泛影响提供一个平台，鼓励跨学科的对话，包括评论、新闻专题、新闻和观点文章以及通信。

Nat Mach Intell

Autoregressive neural-network wavefunctions for ab initio quantum chemistry

An autoregressive neural- network ansatz is employed that allows them to study larger molecules than previously attempted with neural-network quantum state approaches and can outperform the de facto gold-standard coupled-cluster methods even in the presence of strong quantum correlations.

Category	Quartile
计算机科学	1
计算机科学, 计算机人工智能	1
计算机科学, 计算机跨学科应用	1

Autoregressive neural-network wavefunctions for ab initio quantum chemistry

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Category	Quartile
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	1