NNQS-Transformer: An Efficient and Scalable Neural Network Quantum States Approach for Ab Initio Quantum Chemistry
- Resource Type
- Conference
- Authors
- Wu, Yangjun; Guo, Chu; Fan, Yi; Zhou, Pengyu; Shang, Honghui
- Source
- SC23: International Conference for High Performance Computing, Networking, Storage and Analysis High Performance Computing, Networking, Storage and Analysis, SC23: International Conference for. :1-14 Nov, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Technological innovation
Costs
Quantum chemistry
Scalability
High performance computing
Graphics processing units
Artificial neural networks
many-body Schrödinger equation
neural network quantum state
transformer based architecture
autoregressive sampling
- Language
- ISSN
- 2167-4337
Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for ab initio electronic structure calculations. The major innovations include: (1) A transformer based architecture as the quantum wave function ansatz; (2) A data-centric parallelization scheme for the variational Monte Carlo (VMC) algorithm which preserves data locality and well adapts for different computing architectures; (3) A parallel batch sampling strategy which reduces the sampling cost and achieves good load balance; (4) A parallel local energy evaluation scheme which is both memory and computationally efficient; (5) Study of real chemical systems demonstrates both the superior accuracy of our method compared to state-of-the-art and the strong and weak scalability for large molecular systems with up to 120 spin orbitals.