Development of a ReaxFF description for gold

Järvi, Tommi T.; Kuronen, Antti; Hakala, M.; Nordlund, K.; Duin, Adri C. T. van; Goddard, William A.; Jacob, Timo

doi:10.1140/epjb/e2008-00378-3

Cited by 61 publications

(73 citation statements)

References 47 publications

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“…Bond order‐based reactive force fields, such as Tersoff, Brenner, and ReaxFF potentials, differ from classical force fields, such as UFF, CHARMM, or AMBER, which require that defined bonds remain fixed over the course of a simulation. ReaxFF potentials developed for Au and other metals normally use three separate energy terms as seen in the following equation:

E_{total} = E_{bond} + E_{over} + E_{vdw},

where E bond is for bond energies of atom pairs, E over is an energy penalty to prevent overcoordination, and E vdw accounts for van der Waals interactions and interatomic repulsions when interatomic distances are too small. ReaxFF potentials can also be parameterized to include three‐body terms which provide energy contributions from valence angles between sets of three Au atoms.…”

Section: Methodsmentioning

confidence: 99%

Neural network and ReaxFF comparison for Au properties

Boes

Groenenboom

Keith

et al. 2016

Int. J. Quantum Chem.

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We have studied how ReaxFF and Behler–Parrinello neural network (BPNN) atomistic potentials should be trained to be accurate and tractable across multiple structural regimes of Au as a representative example of a single‐component material. We trained these potentials using subsets of 9,972 Kohn‐Sham density functional theory calculations and then validated their predictions against the untrained data. Our best ReaxFF potential was trained from 848 data points and could reliably predict surface and bulk data; however, it was substantially less accurate for molecular clusters of 126 atoms or fewer. Training the ReaxFF potential to more data also resulted in overfitting and lower accuracy. In contrast, BPNN could be fit to 9,734 calculations, and this potential performed comparably or better than ReaxFF across all regimes. However, the BPNN potential in this implementation brings significantly higher computational cost. © 2016 Wiley Periodicals, Inc.

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E_{total} = E_{bond} + E_{over} + E_{vdw},

Section: Methodsmentioning

confidence: 99%

Neural network and ReaxFF comparison for Au properties

Boes

Groenenboom

Keith

et al. 2016

Int. J. Quantum Chem.

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“…The projector augmented wave (PAW) method is used for efficient description of valence states near the cores. The Perdew-Burke-Ernzerhof (PBE)55 parameterization of the generalized gradient approximation (GGA) is selected for electron exchange-correlation since this functional is found to perform well for nanoclusters5657. In fact, PBE is shown to reproduce the CCSD(T) (coupled cluster with full single/double excitations and many-body perturbation theory estimate of triple excitation) and B2PLYP (doubly hybrid functional with perturbation corrections) results for the structure of Au 8 56.…”

Section: Methodsmentioning

confidence: 99%

Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search

Kınacı

Narayanan

Şen

et al. 2016

Sci Rep

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Au nanoclusters are of technological relevance for catalysis, photonics, sensors, and of fundamental scientific interest owing to planar to globular structural transformation at an anomalously high number of atoms i.e. in the range 12–14. The nature and causes of this transition remain a mystery. In order to unravel this conundrum, high throughput density functional theory (DFT) calculations, coupled with a global structural optimization scheme based on a modified genetic algorithm (GA) are conducted. More than 20,000 Au12, Au13, and Au14 nanoclusters are evaluated. With any DFT functional, globular and planar structures coexist across the size range of interest. The planar-globular transition is gradual at room temperature rather than a sharp transition as previously believed. The effects of anionicity, s-d band hybridization and long range interactions on the dimensional transition are quantified by using the structures adjacent to the minima. Anionicity marginally changes the relative stability of the clusters. The degree of s-d hybridization is varied via changing the Hubbard U value which corroborate that s-d hybridization alone does not stabilize planar structures. van der Waals interactions, on the other hand, stabilize globular structures. These results elucidate the balance between the different reasons of the dimensional transition in gold nanoclusters.

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“…Reactive FFs such as ReaxFF (originally developed for hydrocarbons) allow for bond formation and cleavage to be updated “on the fly” during MD simulations and as such can be used to model chemical reactions like gold surface reconstructions upon thiolation . In 2008, ReaxFF parameters for gold were developed and subsequently expanded to describe Au–S–C–H systems . More recently, a modified version of ReaxFF has been applied to study the binding mechanism of cysteine to gold, which confirmed the experimentally determined two‐step binding process of an initial slow physisorption followed by a fast chemisorption and the formation of the “staple” motif .…”

Section: All‐atom Molecular Mechanics and Dynamics (Force Field Methods)mentioning

confidence: 76%

Understanding and Designing the Gold–Bio Interface: Insights from Simulations

Charchar

Christofferson

Todorova

et al. 2016

Small

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Gold nanoparticles (AuNPs) are an integral part of many exciting and novel biomedical applications, sparking the urgent need for a thorough understanding of the physicochemical interactions occurring between these inorganic materials, their functional layers, and the biological species they interact with. Computational approaches are instrumental in providing the necessary molecular insight into the structural and dynamic behavior of the Au-bio interface with spatial and temporal resolutions not yet achievable in the laboratory, and are able to facilitate a rational approach to AuNP design for specific applications. A perspective of the current successes and challenges associated with the multiscale computational treatment of Au-bio interfacial systems, from electronic structure calculations to force field methods, is provided to illustrate the links between different approaches and their relationship to experiment and applications.

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Development of a ReaxFF description for gold

Cited by 61 publications

References 47 publications

Neural network and ReaxFF comparison for Au properties

Neural network and ReaxFF comparison for Au properties

Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search

Understanding and Designing the Gold–Bio Interface: Insights from Simulations

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