Towards structural optimization of gold nanoclusters with quantum Monte Carlo
Tiihonen, J., & Häkkinen, H. (2023). Towards structural optimization of gold nanoclusters with quantum Monte Carlo. Journal of Chemical Physics, 159(17), Article 174301. https://doi.org/10.1063/5.0174383
Julkaistu sarjassa
Journal of Chemical PhysicsPäivämäärä
2023Tekijänoikeudet
© 2023 Author(s). Published under an exclusive license by AIP Publishing.
We study the prospects of using quantum Monte Carlo techniques (QMC) to optimize the electronic wavefunctions and atomic geometries of gold compounds. Complex gold nanoclusters are widely studied for diverse biochemical applications, but the dynamic correlation and relativistic effects in gold set the bar high for reliable, predictive simulation methods. Here we study selected ground state properties of fewatom gold clusters by using density functional theory (DFT) and various implementations of the variational Monte Carlo (VMC) and diffusion Monte Carlo. We show that the QMC methods mitigate the exchange-correlation (XC) approximation made in the DFT approach: the average QMC results are more accurate and significantly more consistent than corresponding DFT results based on different XC functionals. Furthermore, we use demonstrate structural optimization of selected thiolated gold clusters with between 1 and 3 gold atoms using VMC forces. The optimization workflow is demonstrably consistent, robust, and its computational cost scales with n b , where b < 3 and n is the system size. We discuss the implications of these results while laying out steps for further developments.
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AIP PublishingISSN Hae Julkaisufoorumista
0021-9606Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/194292700
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Lisätietoja rahoituksesta
The work has been performed under the Project HPC-EUROPA3 (Grant No. INFRAIA-2016-1- 730897), with the support of the EC Research Innovation Action under the H2020 Programme. We thank SURF (www.surf.nl) for the support in using the National Supercomputer Snellius. We acknowledge grants of computer capacity from the Finnish Grid and Cloud Infrastructure (persistent identifier Grant No. urn:nbn:fi:researchinfras-2016072533). The authors also wish to acknowledge CSC–IT Center for Science, Finland, for computational resources. This work was supported by the Academy of Finland. ...Lisenssi
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