混合张量网络和神经网络 量子化学的量子状态
Zibo Wu1, Bohan Zhang1, Wei-Hai Fang1
1Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
Journal of chemical theory and computation
|October 9, 2025
概括
神经网络量子状态 (NQS) 现在在分子模拟中实现化学准确性,使用一种新的有限度图反复神经网络 (BDG-RNN) 和神经网络相关器 (NNC). 这些进步,加上高效的能源评估,提高了量子化学应用的NQS.
科学领域:
- 量子多体物理学 量子多体物理学
- 计算化学是一种计算化学.
- 在量子力学中的机器学习.
背景情况:
- 神经网络量子状态 (NQS) 对量子多体问题具有强大作用,但在分子系统中面临挑战.
- 现有的NQS方法需要提高电子结构计算的准确性和效率.
研究的目的:
- 引入创新,克服分子系统NQS的局限性.
- 提高NQS在量子化学中的适用性和准确性.
主要方法:
- 开发了一个有限度图反复神经网络 (BDG-RNN) 替代品,将张量和神经网络状态混合为分子适用性.
- 引入神经网络相关器 (NNC),包括cos-RBM和Ising-RBM,以提高波函数的表达性和精度.
- 实现了一种半静态算法,用于高效的局部能源评估,降低计算成本.
主要成果:
- 在具有挑战性的分子系统中取得化学精度,如H50,[Fe2S2(SCH3)4]2-,和H18.
- 证明了BDG-RNN替代品和NNC在提高NQS性能方面的有效性.
- 验证了半静态能量评估的计算效率和准确性.
结论:
- 与BDG-RNN和NNC一起开发的NQS框架显著推进了量子化学模拟.
- 这些创新为解决复杂的分子电子结构问题提供了更准确,更有效的方法.
- 开源包PyNQS促进了这些NQS方法的进一步研究和应用.
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