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改进了神经网络量子状态的优化,并对二元体进行了测试

Xiang Li¹, Jia-Cheng Huang¹, Guang-Ze Zhang¹

¹Department of Chemistry and Engineering Research Center of Advanced Rare-Earth Materials of Ministry of Education, Tsinghua University, Beijing 100084, China.

The Journal of chemical physics

|June 17, 2024

概括

神经网络量子状态 (NQS) 增强了量子化学计算. 变量蒙特卡洛 (VMC) 的算法改进降低了计算成本,并提高了诸如二元体等复杂分子的精度.

科学领域:

量子化学是一个量子化学.
计算物理计算物理
机器学习在科学中的应用

背景情况:

神经网络量子状态 (NQS) 已经成为代表波函数的强大工具.
变量蒙特卡洛 (VMC) 方法是计算密集型的,特别是复杂的电子结构.
对NQS的高效优化对于推进它们在量子化学中的应用至关重要.

研究的目的:

用NQS引入算法增强来降低VMC优化的计算成本.
为了提高优化大规模限制波兹曼机器 NQS 的效率和稳定性.
证明增强的NQS在量子化学中的实际适用性.

主要方法:

开发和实施三种算法增强:自适应性学习速率,受约束优化和块优化.
将精细的VMC-NQS算法应用于H2O和N2的多引用键延伸 (cc-pVDZ基础集).
对强相关的二元体 (Cr2) (Ahlrichs SV基础集) 的基态能量计算.

主要成果:

增强的VMC-NQS算法显著降低了计算需求.
与合集群理论相比,在H2O和N2键延伸方面取得了更高的准确性.
成功地以较低的计算成本高精度计算了Cr2二次元的基本状态能量.

结论:

拟议的算法改进大大提高了NQS优化的效率和稳定性.
这项工作为更有效地优化大规模NQS,特别是受限制的博尔兹曼机器铺平了道路.
这些发现代表了NQS在量子化学中的实际应用的重大进步.

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