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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
Published on: April 12, 2019
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Implementing advanced trial wave functions in fermion quantum Monte Carlo via stochastic sampling.
Zhi-Yu Xiao1, Tao Xiang1, Zixiang Lu2
1Institute of Physics, Chinese Academy of Sciences, P.O. Box 603, Beijing 100190, China.
The Journal of Chemical Physics
|October 22, 2025
Summary
We developed an efficient method to implement complex correlated wave functions in auxiliary-field quantum Monte Carlo (AFQMC). This approach improves accuracy and efficiency for calculating ground-state energies in molecules, achieving chemical accuracy.
Area of Science:
- Computational Physics
- Quantum Chemistry
- Many-Body Theory
Background:
- Auxiliary-field quantum Monte Carlo (AFQMC) methods are powerful for studying many-body systems.
- The accuracy of AFQMC is often limited by the quality of the trial wave function used to control the sign/phase problem.
- Implementing sophisticated, correlated trial wave functions in AFQMC has been computationally challenging.
Purpose of the Study:
- To introduce an efficient and general method for implementing correlated many-body trial wave functions in AFQMC.
- To demonstrate that advanced wave functions, previously thought to require quantum computers, can be used with classical algorithms.
- To improve the accuracy and efficiency of AFQMC calculations for ground-state properties.
Main Methods:
- Developed a novel approach to implement correlated trial wave functions, expressed as integrals over auxiliary variables times Slater determinants.
- Coupled random walkers to a generalized Metropolis sampling algorithm for stochastically sampling these trial wave functions.
- Preserved the original computational scaling of AFQMC while incorporating complex wave function forms.
Main Results:
- Achieved significant improvements in both accuracy and efficiency compared to standard trial wave functions.
- Demonstrated that the method yields total ground-state energies within chemical accuracy for challenging molecular systems.
- Successfully tested the approach on molecules undergoing bond stretching and in transition metal diatomics.
Conclusions:
- The introduced method enables the efficient use of a broad class of correlated trial wave functions in AFQMC.
- This work overcomes previous limitations in implementing advanced wave functions, enhancing AFQMC's applicability.
- The method is a significant step towards incorporating machine learning-based wave functions and other advanced forms in quantum Monte Carlo simulations.
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