Towards structural optimization of gold nanoclusters with quantum Monte Carlo: Data repository

This dataset contains the raw data and postprocessing data for the following scientific article: Juha Tiihonen, Hannu Häkkinen Towards structural optimization of gold nanoclusters with quantum Monte Carlo The Journal of Chemical Physics (2023) Abstract: 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 few-atom gold clusters by using DFT and various implementations of the variational Monte Carlo (VMC) and diffusion Monte Carlo. We show that the QMC methods mitigate the 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. The data is numerical simulation data, simulation input files, software source code, postprocessing scripts, processed data and plots. The main simulation methods are CHAMP and QMCPACK, workflows are managed by Nexus, and postprocessing is done in Python. Details of the data are described in README files.