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Updated: Jun 21, 2025
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Transformer-Based Neural-Network Quantum State Method for Electronic Band Structures of Real Solids
Lizhong Fu1, Yangjun Wu1, Honghui Shang1
1Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China.
Journal of Chemical Theory and Computation
|July 8, 2024
Summary
QiankunNet-Solid introduces periodic boundary conditions to neural network quantum states for accurate solid material calculations. This method achieves high accuracy, comparable to established techniques, even for strongly correlated systems.
Area of Science:
- Computational physics
- Quantum chemistry
- Materials science
Background:
- Neural networks show promise for electron correlation problems.
- Existing methods struggle with periodic boundary conditions in solids.
Purpose of the Study:
- To develop a neural network quantum state (NNQS) method for solid materials.
- To enable accurate ab initio calculations for periodic systems.
Main Methods:
- Proposed QiankunNet-Solid, integrating periodic boundary conditions into NNQS.
- Utilized a generative Transformer architecture and batched autoregressive sampling (BAS).
Main Results:
- Demonstrated accuracy in 1D, 2D, and 3D periodic systems.
- Achieved results comparable to full configuration interaction and coupled-cluster methods.
- Computed band structures and density of states for silicon.
Conclusions:
- QiankunNet-Solid effectively handles periodic boundary conditions in NNQS.
- Enables accurate and efficient electronic structure studies for solids.
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