1. Improved optimization for the neural-network quantum states and tests on the chromium dimer.
- Author
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Li, Xiang, Huang, Jia-Cheng, Zhang, Guang-Ze, Li, Hao-En, Shen, Zhu-Ping, Zhao, Chen, Li, Jun, and Hu, Han-Shi
- Subjects
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QUANTUM states , *MACHINE learning , *CHROMIUM , *WAVE functions , *QUANTUM chemistry - Abstract
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 H2O and N2 within the cc-pVDZ basis set and calculate the ground-state energy of the strongly correlated chromium dimer (Cr2) 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-based NQS more effectively and marking a substantial advancement in NQS's practical quantum chemistry applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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