Low-lying Ptn cluster structures (n = 6–10) from global optimizations based on DFT potential energy surfaces: Sensitivity of the chemical ordering with the functional

Li, Rui; Odunlami, Marc; Carbonnière, Philippe

doi:10.1016/j.comptc.2017.02.010

Cited by 17 publications

(24 citation statements)

References 51 publications

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“…Various structure types were thoroughly studied by Kumar and Kawazoe using manual construction up to n = 38 in a continuous manner with selected additional representatives up to several hundred atoms. Also, many other studies reported the globally optimized DFT structures up to n = 20 ,,,, (see also the review and references therein). These studies demonstrate that the GM structures of some Pt clusters significantly depend on the DFT functional applied.…”

Section: Resultsmentioning

confidence: 99%

“…This implies descent to the local minimum (LM) closest to the selected starting point. More systematic search for the global minimum (GM) structure and low-lying local minima (LMs) in different spin states has been carried out only for some selected clusters of small nuclearities. − Additionally, the structures and energies of platinum clusters using different techniques of structure generations including MD, GO, manual building, and empirical potential (EP) pre-optimization were studied in refs ,,,− for many nuclearities in the range from 2 to 55 (some representatives up to 344 atoms in ref and cuboctahedra up to 1485 atoms in ref ). Serious complications in such studies include dependence of the cluster structure and energy on the electronic spin multiplicity.…”

Section: Introductionmentioning

confidence: 99%

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Global Structure Optimization of Pt Clusters Based on the Modified Empirical Potentials, Calibrated using Density Functional Theory

et al. 2019

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The geometry of platinum clusters Pt n (n = 2–10, 13, 19, 24, 38, 55, and 75) was optimized at the UBLYP/CRENBS, UBPW91/CRENBS, UBPW91/LANL2DZ, and UPBE0/LANL08 density functional theory (DFT) levels for different spin states in order to establish their structure and thermodynamic properties and obtain reference data for the calibration of interatomic potentials. The optimized cluster structures and energies were used to elaborate the atomic parameters of the empirical potentials of Gupta and Sutton–Chen, and modified versions of these potentials SCG5 and SCG5A were developed for better description of medium-range interactions. The new DFT-calibrated potentials were used for the global optimization of the cluster structure in the broad nuclearity range of n = 3–150 using the modified methods of artificial bee colony and Metropolis Monte Carlo simulated annealing. The structures obtained during the global optimization with modified potentials show a much higher tendency to form decahedral and face-centered cubic structures at lower n instead of icosahedral ones found with classical metallic potentials.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global Structure Optimization of Pt Clusters Based on the Modified Empirical Potentials, Calibrated using Density Functional Theory

et al. 2019

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“…Heredia, (Heredia, et al, 2012), Hamad (Hamad, et al, 2014) y Li, (Li, Odunlami & Carbonnière, 2017), propusieron diferentes combinaciones de funcional/base, programas de cómputo y analizaron el efecto de la multiplicidad, concluyendo que en la mayoría de los cúmulos de Ptn donde n<12 las conformaciones obtenidas prevalecen, mientras que para los de mayor tamaño (n>12) existen discrepancias.…”

Section: Platinounclassified

Estudio TFD de cúmulos de Pt-Ir con geometría prisma triangular

Rangel-Peña

Zárate-Hernández

Camacho-Mendoza

et al. 2021

ICBI

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En este trabajo, se presenta un estudio sistemático de las propiedades estructurales y electrónicas de cúmulos bimetálicos de Pt6−nIrn donde n = [0 – 6] usando la metodología B3LYP/LanL2DZ en el contexto de la teoría de los funcionales de la densidad (TFD). Las estructuras de los cúmulos puros Pt6 e Ir6 muestran arreglos tridimensionales altamente estables con multiplicidades electrónicas (μ) entre 7 y 13. El cúmulo puro de Pt6 más estable adopta una geometría de prisma triangular. Para la evaluación de los cúmulos dopados Pt6−nIrn (n = [0 – 6]) se determinó la longitud de enlace promedio, las cargas de Mulliken, los orbitales moleculares frontera y el mapeo del potencial electrostático. La evaluación de las propiedades electrónicas y estructurales demuestra que la incorporación de átomos de iridio en cúmulos de platino modifica su reactividad.

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“…TPSSh is a hybrid version of meta-GGA TPSS functional, showing very good accuracy for Pt clusters. [57][58] optPBE-vdW is based on the non-local correlation functional from the Rutgers-Chalmers van der Waals Density Functional (vdW-DF) combined with an optimized PBE-like exchange functional. [59][60] It correctly describes adsorption properties of Pt.…”

Section: Transferability Of δHdnnp In Out-of-sample Datamentioning

confidence: 99%

Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks

Sun

Sautet

2019

J. Chem. Theory Comput.

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Data-driven machine learning force fields (MLF) are more and more popular in atomistic simulations, and exploit machine learning methods to predict energies and forces for unknown structures based on the knowledge learned from an existing reference database. The latter usually comes from density functional theory calculations. One main drawback of MLFs is that physical laws are not incorporated in the machine learning models and instead, MLFs are designed to be very flexible to simulate complex quantum chemistry potential energy surface (PES). In general, MLFs have poor transferability, and hence a very large trainset is required to span all the target feature space to get a reliable MLF. This procedure becomes more troublesome when the PES is complicated, with a large number of degrees of freedom, in which building a large database is inevitable and very expensive, especially when accurate but costly exchange-correlation functionals have to be used. In this manuscript, we exploit a high dimensional neural network potential (HDNNP) on Pt clusters of size 6 to 20 as one example. Our standard level of energy calculation is DFT GGA (PBE) using a plane wave basis set. We introduce an approximate but fast level with the PBE functional and a minimal atomic orbital basis set, then a more accurate but expensive level, using a hybrid functional or non-local vdw functional and a plane wave basis set, is reliably predicted by learning the difference with HDNNP. The results show that such a differential approach (named ΔHDNNP) can deliver very accurate predictions (error < 10 2 meV/atom) in reference to converged basis set energies as well as more accurate but expensive xc functional. The overall speedup can be as large as 900 for 20 atom Pt cluster. More importantly, ΔHDNNP shows much better transferability due to the intrinsic smoothness of delta potential energy surface, and accordingly one can use much smaller trainset data to obtain better accuracy than the conventional HDNNP. A multi-layer ΔHDNNP is thus proposed to obtain very accurate predictions versus expensive non-local vdW functional calculations in which the required trainset is further reduced. The approach can be easily generalized to any other machine learning methods and opens a path to study the structure and dynamics of Pt clusters and nanoparticles.

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Low-lying Ptn cluster structures (n = 6–10) from global optimizations based on DFT potential energy surfaces: Sensitivity of the chemical ordering with the functional

Cited by 17 publications

References 51 publications

Global Structure Optimization of Pt Clusters Based on the Modified Empirical Potentials, Calibrated using Density Functional Theory

Global Structure Optimization of Pt Clusters Based on the Modified Empirical Potentials, Calibrated using Density Functional Theory

Estudio TFD de cúmulos de Pt-Ir con geometría prisma triangular

Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks

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