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Numerically exact configuration interaction at quadrillion-determinant scale

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Pubblicato in:Nature Communications vol. 16, no. 1 (2025), p. 11016-11028
Autore principale: Shayit, Agam
Altri autori: Liao, Can, Upadhyay, Shiv, Hu, Hang, Zhang, Tianyuan, DePrince III, A. Eugene, Yang, Chao, Li, Xiaosong
Pubblicazione:
Nature Publishing Group
Soggetti:
Benchmarks
Computer memory
Computer peripherals
Combinatorial analysis
Hilbert space
Configuration interaction
Quantum chemistry
Tensors
Nodes
Decomposition
Computing costs
Computer applications
Algorithms
Compression
Determinants
Floating point arithmetic
Wave functions
Environmental
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Abstract:The combinatorial growth of configuration interaction (CI) has long limited this formally exact quantum chemistry method to only the smallest molecules. Here, we report a numerically exact CI calculation exceeding one quadrillion (1015) determinants, made possible by a lossless categorical compression strategy within the small-tensor-product distributed active space (STP-DAS) framework. This approach overcomes the traditional memory bottlenecks of CI by a numerically exact compression of the wavefunction representation and reformulating the most computationally demanding matrix–vector operations. Using this method, we performed a fully relativistic CI calculation of the ground state of HBrTe with over 1015 complex-valued determinants in just 34.5 h on 1000 computing nodes—the largest CI calculation ever reported. We further achieved fast computation for systems with hundreds of billions of determinants on only a few compute nodes. Extensive benchmarks confirm that the method retains full numerical exactness while cutting memory and computational cost by orders of magnitude. Compared to previous state-of-the-art CI calculations, this work achieves a 1000 times increase in CI space, a 106-fold increase in floating-point operations performed, and a 106-fold improvement in computational speed.Due to the combinatorial scaling of configuration interaction methods, formally exact quantum chemistry results are only available for small systems. Here, the authors present an implementation using categorical compression, enabling efficient modeling of many electron systems.
ISSN:2041-1723
DOI:10.1038/s41467-025-65967-7
Fonte:Health & Medical Collection