用自注意力机制求解关联电子问题，效果好且无需人工干预。
Is attention all you need to solve the correlated electron problem?
- 用大规模自注意力神经网络构造多体波函数变分形式。
- 参数量随电子数平方增长，可高效模拟大体系。
- 在莫尔量子材料中实现无偏精准求解，适合复杂电子系统研究。
注意力机制通过学习对象间关系彻底改变了人工智能研究。本文探讨了基于大规模参数自注意力神经网络构建的多体波函数变分形式在解决固体中相互作用电子问题中的应用。通过对莫尔量子材料进行系统的神经网络变分蒙特卡洛研究，我们证明自注意力变分形式能提供准确且高效的解决方案，且无须人为先验假设。此外，数值研究表明所需变分参数数量大致随电子数的平方 $N^2$ 增长，为大规模高效模拟开辟了新路径。
原文摘要 · Abstract (English)
The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter self-attention neural network can be used to solve the interacting electron problem in solids. By a systematic neural-network variational Monte Carlo study on a moiré quantum material, we demonstrate that the self-attention ansatz provides an accurate and efficient solution without human bias. Moreover, our numerical study finds that the required number of variational parameters scales roughly as $N^2$ with the number of electrons, which opens a path towards efficient large-scale simulations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。