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Related Experiment Video

Updated: Jul 20, 2026

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Monte Carlo explicitly correlated many-body Green's function theory.

Cole M Johnson¹, Alexander E Doran¹, Seiichiro L Ten-No²

¹Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

The Journal of Chemical Physics

|November 10, 2018

Summary

A new stochastic algorithm efficiently computes corrections for Green

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Last Updated: Jul 20, 2026

Setting Limits on Supersymmetry Using Simplified Models

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Published on: November 15, 2013

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Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

Computational chemistry
Quantum many-body theory

Background:

Basis set incompleteness is a significant error source in quantum chemical calculations.
Accurate computation of self-energy corrections is crucial for reliable predictions.
Explicitly correlated (F12) methods improve accuracy but can be computationally demanding.

Purpose of the Study:

To develop a scalable stochastic algorithm for basis-set-incompleteness corrections in GF2 theory.
To implement and evaluate the Monte Carlo approach for F12 corrections.
To assess the computational efficiency and accuracy of the proposed method.

Main Methods:

Stochastic algorithm utilizing Monte Carlo integration with importance sampling.
Direct evaluation of high-dimensional integrals for F12 corrections.
Parallel implementation on graphical processing units (GPUs) and central processing units (CPUs).

Main Results:

The stochastic algorithm achieves a computational cost scaling of N^4, an improvement over N^5 for deterministic methods.
Accurate calculations of ionization energies for C60 and C70 were performed with low statistical errors.
Basis-set-incompleteness corrections accounted for 50%-60% of deviations from experimental values.

Conclusions:

The proposed stochastic method offers a highly scalable and efficient approach for F12 corrections.
Higher-order perturbation corrections are necessary to fully explain discrepancies with experimental data.
The method is parallelizable, memory-efficient, and versatile regarding correlation factors.