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Related Experiment Video

Updated: Jan 10, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction.

Bowen Kan1,2, Honghui Shang3

  • 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.

Journal of Chemical Theory and Computation
|November 24, 2025
PubMed
Summary
This summary is machine-generated.

A new Generative Transformer Neural Network Selected Configuration Interaction (GTNN-SCI) method accelerates quantum chemistry calculations. This machine learning approach accurately treats complex molecular systems, achieving significant speedups and lower energies than existing methods.

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Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • Machine Learning

Background:

  • Quantum many-body calculations face computational limits due to the exponential growth of configuration space.
  • Accurate treatment of strongly correlated systems is computationally prohibitive for traditional methods.

Purpose of the Study:

  • To introduce a novel machine learning approach, Generative Transformer Neural Network Selected Configuration Interaction (GTNN-SCI), for accelerating quantum chemistry calculations.
  • To enhance the accuracy and efficiency of treating strongly correlated systems.

Main Methods:

  • Developed GTNN-SCI, a Transformer-based machine learning method that generatively samples important configurations.
  • Leveraged the Transformer architecture's self-attention mechanism to capture long-range electron correlations.
  • Applied GTNN-SCI to calculate correlation and binding energies for molecules (N2, H2O, C2) and a challenging [2Fe-2S] cluster.

Main Results:

  • GTNN-SCI achieved up to a 10x speedup compared to state-of-the-art neural network methods.
  • Demonstrated faster convergence and lower energies than previous neural network-based selected CI techniques.
  • Accurately treated the strongly correlated [2Fe-2S] cluster, achieving ground-state energies within chemical accuracy of DMRG benchmarks.

Conclusions:

  • GTNN-SCI combines deep learning with high-performance electronic structure computation for efficient and precise solutions.
  • The generative approach identifies higher-order excitations missed by conventional methods, yielding lower variational energies.
  • GTNN-SCI offers a powerful new avenue for solving the electronic Schrödinger equation in challenging molecular systems.