Error Cancellation in Diffusion Monte Carlo Calculations of Surface Chemistry
Gopal R. Iyer, Brenda M. Rubenstein

TL;DR
This paper demonstrates that leveraging error cancellation in Diffusion Monte Carlo calculations significantly reduces computational costs while maintaining accuracy in surface chemistry applications, such as adsorption energies and potential energy surfaces.
Contribution
The study introduces strategies to exploit many-body finite-size error cancellation in DMC, enabling more efficient and accurate calculations for catalytic surface systems.
Findings
Error cancellation enables order-of-magnitude speedups in DMC calculations.
DMC accurately predicts adsorption site preferences consistent with experiments.
Mapping potential energy surfaces reveals diffusion pathways and energy barriers.
Abstract
Diffusion Monte Carlo (DMC) is being recognized as a higher-accuracy, albeit more computationally expensive, alternative to Density Functional Theory (DFT) for energy predictions of catalytic systems. A major computational bottleneck in the use of DMC for catalysis is the need to perform finite-size extrapolations by simulating increasingly large periodic cells (supercells) to eliminate many-body finite-size effects and obtain energies in the thermodynamic limit. Here, we show that this computational cost can be significantly reduced by leveraging the cancellation of many-body finite-size errors that accompanies the evaluation of energy differences when calculating quantities like binding energies and mapping potential energy surfaces. We test the error cancellation and convergence in two well-known adsorbate/slab systems, H2O/LiH(001) and CO/Pt(111). Based on this, we identify…
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Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Catalysts for Methane Reforming
