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NNQS-Transformer: An Efficient and Scalable Neural Network Quantum States Approach for Ab Initio Quantum Chemistry
DescriptionNeural 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.
(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.
Event Type
Paper
TimeWednesday, 15 November 202311:30am - 12pm MST
Location301-302-303
Applications
Modeling and Simulation
