NNQS-Transformer: An Efficient and Scalable Neural Network Quantum States Approach for Ab Initio Quantum Chemistry

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 calculati...

Celý popis

Podrobná bibliografie

Vydáno v:	International Conference for High Performance Computing, Networking, Storage and Analysis (Online) s. 1 - 14
Hlavní autoři:	Wu, Yangjun, Guo, Chu, Fan, Yi, Zhou, Pengyu, Shang, Honghui
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	ACM 11.11.2023
Témata:	Artificial neural networks autoregressive sampling Costs Graphics processing units High performance computing many-body Schrödinger equation neural network quantum state Quantum chemistry Scalability Technological innovation transformer based architecture
ISSN:	2167-4337
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!