Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network

Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense m...

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Bibliographic Details
Published inJournal of chemical theory and computation Vol. 18; no. 7; pp. 4255 - 4268
Main Authors Finkelstein, Joshua, Rubensson, Emanuel H., Mniszewski, Susan M., Negre, Christian F. A., Niklasson, Anders M. N.
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 12.07.2022
Subjects
Artificial neural networks
Beräkningsvetenskap
Charge density
Chemical calculations
Computing costs
Electron correlation
Floating point arithmetic
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Layers
Mathematics
Neural networks
Order
Perturbation theory
Quantum Electronic Structure
Scientific Computing
Tensors
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Summary:Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense matrix–matrix multiplications, in mixed-precision arithmetics, which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree–Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low-precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.
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89233218CNA000001
LA-UR-22-21647
USDOE Laboratory Directed Research and Development (LDRD) Program
USDOE National Nuclear Security Administration (NNSA)
ISSN:1549-9618
1549-9626
1549-9626
DOI:10.1021/acs.jctc.2c00274