An evaluation of the performances of several known low-cost methods for the reproduction of structural features of differently sized Pt nanoparticles (NPs) is presented. The full density functional theory PBE-dDsC functional (within the planewave formalism) was employed to benchmark the semiempirical tight-binding DFTB and GFNn-xTB (n = 0, 1, 2) and the reactive force-field ReaxFF. Performances were evaluated by comparing several size-dependent features (such as relative stabilities, structural descriptors, and vibrational features) computed with the different methods. Various structures (ordered and amorphous) and sizes (from Pt13 to Pt561) were considered in the datasets. ReaxFF molecular dynamics (MD) was employed to achieve the amorphization of cuboctahedral Pt147, Pt309, and Pt561 geometries, which were subsequently optimized with both the low-cost methods and the DFT reference, within a multilevel modeling approach. The structures were further annealed with GFN0-xTB MD. While DFTB performs quite well over all the selected structures, GFN2- xTB and the cheaper GFN0-xTB show a general predilection for amorphous geometries. The performances of GFN2-xTB are found to worsen with the increasing size of the system, while ReaxFF and GFN0-xTB undergo the opposite trend. We suggest that the semiempirical DFTB (and within certain limitations GFN0-xTB and ReaxFF) could be suited for fast screening through amorphous big-sized Pt NPs.
Authors
Alberto Ricchebuono, Eleonora Vottero, Andrea Piovano, Elena Groppo, Pascal Raybaud, Cรฉline Chizallet
Related Organizations
- Bibliographic Reference
- Alberto Ricchebuono, Eleonora Vottero, Andrea Piovano, Elena Groppo, Pascal Raybaud, et al.. Assessing Low-Cost Computational Methods against Structural Properties and Size Effects of Pt nanoparticles. Journal of Physical Chemistry C, 2023, 127 (37), pp.18454-18465. ⟨10.1021/acs.jpcc.3c02799⟩. ⟨hal-04289810⟩
- DOI
- https://doi.org/10.1021/acs.jpcc.3c02799
- Funding
- This work was performed using HPC resources from the supercomputers ENER440 of IFPEN, Fram of the Norwegian academic HPC infrastructure (for which we thank Prof. Stian Svelle and Prof. Silvia Bordiga), and OCCAM of the University of Torino.
- HAL Collection
- IFP Energies Nouvelles
- HAL Identifier
- 4289810
- Institution
- ['Universitร degli studi di Torino = University of Turin', 'Institut Laue-Langevin', 'IFP Energies nouvelles']
- Published in
- France
Table of Contents
- Alberto Ricchebuono Eleonora Vottero Andrea Piovano Elena Groppo Pascal Raybaud Cรฉline 1
- Chizallet . 1
- S1 Impact of the reference DFT functional 2
- S2 CPU times 3
- S3 Pt small dataset Geometries complete histograms 4
- S4 Pt small dataset regression analysis on relative stabilities 5
- S5 Pt small dataset Thermochemical parameters H 7
- S6 Pt small dataset Relative stabilities corrected for vibrational contributions 8
- S7 Pt and Pt amorphous datasets Kernel Density Estimation 9
- If is a discrete variable here bond lengths angles and radius of gyration in each nanoparticle assuming a 9
- ๐ ๐ ๐ฅ ๐ฅ ๐ฅ ๐ 9
- Kernel Density Estimator 9
- ๐ ๐ฅ 9
- 1 ๐๐ 9
- ๐พ๐ฅ ๐ฅ 9
- Where is a smoothing parameter and is the Kernel function. Since the Seaborn library exploits a Gaussian 9
- ๐ ๐พ 9
- Kernel to practically compute KDE plots the estimated probability density function 9
- ๐ ๐ฅ 9
- 1 ๐๐ 9
- 1 2๐ ๐ 9
- Where the smoothing parameter assumes the meaning of a bandwidth tuner for the Gaussians. 9
- 0.12 1.8 0.0075 9
- Bond length r DFTB GFN0-xTB GFN2-xTB ReaxFF 9
- -0.027 0.015 0.001 0.120 0.055 0.038 9
- -0.030 0.004 0.023 0.055 0.043 0.120 0.063 0.335 9
- Bond angle ฮธ DFTB GFN0-xTB GFN2-xTB ReaxFF 9
- -0.245 23.5 -0.565 153.7 0.458 50.7 1.45 390.8 9
- -0.021 7.03 -0.620 67.4 -0.527 133.8 0.787 380.8 9
- Radius of Gyration R DFTB GFN0-xTB GFN2-xTB ReaxFF 10
- -0.034 1 10 -0.014 0.001 0.030 2 10 0.136 0.002 10
- 0.044 9 10 -0.003 4 10 0.040 7 10 0.160 0.001 10
- Since the distributions are not perfect gaussians the reported mean values do not correspond with the maxima 10
- ๐น๐๐ป๐ 2 2ln 2 ๐ 10
- S8 Pt and Pt amorphous datasets correlation analysis for Pt amorphous dataset 11
- S9 Amorphization energy 12
