Controlling the nucleation and growth of ultrasmall metal nanoclusters with MoS<sub>2</sub> grain boundaries

Shi, Yongliang; Rabbani, Muztoba; Vázquez‐Mayagoitia, Álvaro; Zhao, Jin; Saidi, Wissam A.

doi:10.1039/d1nr07836d

Cited by 7 publications

(4 citation statements)

References 59 publications

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“…It is worth noting that clusters on the surface exhibit a fluxional character. – The successful predictions made by our DP in terms of the energy changes along different diffusion paths indicate its effectiveness in distinguishing the energetic disparities between different isomers of clusters. This distinction can be clearly identified from Figure S5, where we take Al 3 as an example and provide a comparison between DFT- and DP-calculated energy differences for different isomers of Al 3 extracted from its diffusion paths in Figure d–f.…”

Section: Resultsmentioning

confidence: 64%

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential

Wu,

Liu,

Ran

et al. 2023

J. Phys. Chem. C

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The adsorption and diffusion behaviors of clusters on surfaces play critical roles in numerous important applications. Potential-based molecular dynamics simulations are a powerful tool to study these behaviors at the atomic scale. However, conventional potentials typically parametrized using bulk or surface properties, fail to accurately describe the intricate surface behavior of clusters due to the complexity of their atomic environments. Here, we develop a specialized machine learning potential (MLP) for describing Al clusters on surfaces, which is related to wide-ranging applications. The MLP development was performed using a workflow that is based on an adaptive iterative learning method and incorporates initialization, generalization, and specialization modules. By utilizing accurate data from density functional theory (DFT) calculations, the MLP achieves an impressive level of accuracy that closely approximates DFT while maintaining a high computational efficiency. The MLP successfully predicts the surface behavior of different Al clusters and a wide range of basic properties of the Al bulk and surfaces. Remarkably, despite being trained without data from Al x (x = 4−6, 12), the MLP accurately predicts the adsorption and diffusion properties of these clusters. This work highlights the capability of MLPs in the large-scale investigation of the surface phenomena of different clusters and provides a robust methodology for constructing accurate MLPs tailored to intricate surface systems.

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Section: Resultsmentioning

confidence: 64%

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential

Wu,

Liu,

Ran

et al. 2023

J. Phys. Chem. C

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“…The most stable structures of these metal clusters have been reported in previous studies. − The structures of these clusters that we have identified using PSO are in perfect agreement with those reported in the previous studies. Ideally, one would like to compare the computationally determined structures with experimental results, as has been done in recent studies, , but a comparison with the structures presented in the literature provides some evidence that the PSO algorithm is good at accurately searching for stable structures. On Sc 8 , N 2 is adsorbed in the side-on mode, while on Cu 5 it is adsorbed in the end-on mode.…”

Section: Resultsmentioning

confidence: 99%

Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

Tsuji,

Yoshioka,

Okazawa

et al. 2023

ACS Omega

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This paper details the use of computational and informatics methods to design metal nanocluster catalysts for efficient ammonia synthesis. Three main problems are tackled: defining a measure of catalytic activity, choosing the best candidate from a large number of possibilities, and identifying the thermodynamically stable cluster catalyst structure. First-principles calculations, Bayesian optimization, and particle swarm optimization are used to obtain a Ti 8 nanocluster as a catalyst candidate. The N 2 adsorption structure on Ti 8 indicates substantial activation of the N 2 molecule, while the NH 3 adsorption structure suggests that NH 3 is likely to undergo easy desorption. The study also reveals several cluster catalyst candidates that break the general trade-off that surfaces that strongly adsorb reactants also strongly adsorb products.

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“…The e–h nonradiative recombination times were obtained by fitting the charge population using the exponential decay function f ( t ) = exp ( − t τ ) ≈ 1 − t / τ . This method has been widely used to study photoexcited carrier dynamics in various systems. − In this calculation, the spin–orbit coupling (SOC) effects on the structural relaxation have not been included to reduce the computational effort because it has been proven that the results of SOC on charge distribution are rather limited …”

Section: Methodsmentioning

confidence: 99%

Ab Initio Time-Domain Study of Charge Relaxation and Recombination in N-Doped Cu₂O

Zhang

Wang

et al. 2023

ACS Omega

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Cu2O is a good photoelectric material with excellent performance, and its crystal structure, electronic structure, and optical properties have been extensively studied. To further illustrate the charge distribution and the carrier transport in this system, the e–h recombination dynamics was studied. It is found that N doping induced a shallower impurity band above the VBM, leading to significant charge localization around the impurity atom. NAMD simulation reveals that the N doping system possesses a longer e–h nonradiative recombination time scale. Therefore, we demonstrate that the formation of the impurity band and charge localization play an essential role in suppressing e–h recombination in N doping systems. This work is conducive for understanding the carrier transport mechanism in N-doped Cu2O.

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Controlling the nucleation and growth of ultrasmall metal nanoclusters with MoS₂ grain boundaries

Abstract: Stabilization of supported nanoclusters is key for different applications, including catalysis and plasmonics. Here we investigate the impact of grain boundaries in MoS2 on the nucleation and growth of Pt...

Cited by 7 publications

References 59 publications

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential

Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

Ab Initio Time-Domain Study of Charge Relaxation and Recombination in N-Doped Cu₂O

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Controlling the nucleation and growth of ultrasmall metal nanoclusters with MoS2 grain boundaries

Abstract: Stabilization of supported nanoclusters is key for different applications, including catalysis and plasmonics. Here we investigate the impact of grain boundaries in MoS2 on the nucleation and growth of Pt...

Cited by 7 publications

References 59 publications

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential

Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

Ab Initio Time-Domain Study of Charge Relaxation and Recombination in N-Doped Cu2O

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Controlling the nucleation and growth of ultrasmall metal nanoclusters with MoS₂ grain boundaries

Ab Initio Time-Domain Study of Charge Relaxation and Recombination in N-Doped Cu₂O