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Published in 2021 at "Chemical Physics Letters"
DOI: 10.1016/j.cplett.2020.138167
Abstract: Abstract Importance sampling in Diffusion Monte Carlo has a long history. However, only recently, simulations of ground state properties have been extended to spaces mapped with non-Cartesian coordinates. We demonstrate that in spaces with nonzero…
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Keywords:
gradient torsion;
monte carlo;
diffusion monte;
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Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c00483
Abstract: We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small data sets (≈60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict…
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Keywords:
machine learning;
monte carlo;
diffusion monte;
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Published in 2021 at "Molecular Physics"
DOI: 10.1080/00268976.2021.1976426
Abstract: Holes are commonly associated with high-dimensional, non-parametric representations of potential energy surfaces (PESs). These are regions of the PES with large negative values. Typically these occur at highly distorted geometries of (in reality) high energy.…
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Keywords:
potential energy;
energy;
monte carlo;
energy surfaces ... See more keywords
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Published in 2018 at "Physical Review B"
DOI: 10.1103/physrevb.98.085138
Abstract: We present density functional embedding for diffusion Monte Carlo calculations (DMC). Using two test systems, an H chain and a Be slab, we demonstrate the feasibility and show that the approach can give high-quality results.…
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Keywords:
density functional;
carlo calculations;
functional embedding;
monte carlo ... See more keywords