Stochastic effective core potentials, improving efficiency using a spin-dependent core definition

文献信息

发布日期 2022-06-22
DOI 10.1039/D2CP01357F
影响因子 3.676
作者

Jonas Feldt, Antoine Bienvenu, Roland Assaraf


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摘要

Numerically cheap single-core subsamplings have been used to build improved estimators for molecular properties in the variational Monte Carlo framework. The resulting estimators depend only on the valence electron positions and can be thought of as an exact effective core potential for the total energy. We are proposing a spin-dependent core definition which enables exploiting these single-core subsamplings (or sidewalks) not only to decrease the variance of the estimators but also to restrict the main variational Monte Carlo dynamics to the valence region. This results mainly in a simplification of the algorithm and additionally in a gain in efficiency as illustrated on alkane chains and silicon clusters. An evaluation of the efficiency on transition metal systems is done using cobalt clusters, a gain of up to two orders of magnitude is achieved compared to a standard all-electron calculation.

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来源期刊

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
自引率: 10.3%
年发文量: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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