تقرير
Improved Optimization for the Neural-network Quantum States and Tests on the Chromium Dimer
| العنوان: | Improved Optimization for the Neural-network Quantum States and Tests on the Chromium Dimer |
|---|---|
| المؤلفون: | Li, Xiang, Huang, Jia-Cheng, Zhang, Guang-Ze, Li, Hao-En, Shen, Zhu-Ping, Zhao, Chen, Li, Jun, Hu, Han-Shi |
| سنة النشر: | 2024 |
| المجموعة: | Physics (Other) Quantum Physics |
| مصطلحات موضوعية: | Physics - Chemical Physics, Quantum Physics |
| الوصف: | The advent of Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research, sparking a resurgence in orbital space variational Monte Carlo (VMC) exploration. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS: an adaptive learning rate algorithm, constrained optimization, and block optimization. We evaluate the refined algorithm on complex multireference bond stretches of $\rm H_2O$ and $\rm N_2$ within the cc-pVDZ basis set and calculate the ground-state energy of the strongly correlated chromium dimer ($\rm Cr_2$) in the Ahlrichs SV basis set. Our results achieve superior accuracy compared to coupled cluster theory at a relatively modest CPU cost. This work demonstrates how to enhance optimization efficiency and robustness using these strategies, opening a new path to optimize large-scale Restricted Boltzmann Machine (RBM)-based NQS more effectively and marking a substantial advancement in NQS's practical quantum chemistry applications. Comment: 13 pages, 9 figures, and 2 tables |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.09280 |
| رقم الأكسشن: | edsarx.2404.09280 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |