Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles

Fronzi, Marco; Amos, Roger D.; Kobayashi, Rika; Matsumura, Naoki; Watanabe, Kazuo; Morizawa, Rafael K.

doi:10.3390/nano12213891

Cited by 8 publications

(7 citation statements)

References 35 publications

(44 reference statements)

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“…Regarding the question of time and size thresholds, in Ref. [ 5 ] we used many data points to determine the ML-IAP of Au

to ensure we were benchmarking at a high accuracy. This made us wonder whether similar accuracy could be achieved with less data.…”

Section: Results and Discussionmentioning

confidence: 99%

“…In our previous investigation [ 5 ], we performed extensive MD calculations on the lowest energy structure of the Au

nanocluster ( Figure 1 ) and two isomers with the Vienna Ab Initio Simulation Package (VASP) [ 16 , 17 ], to use as training set to build an interatomic potential. The DeePMD package [ 18 , 19 ] was then used to parameterise the potential and calculate properties with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [ 20 ] package.…”

Section: Computational Detailsmentioning

confidence: 99%

“…Machine Learning Interatomic Potentials (ML-IPs) directly target the potential energy surface through neural networks, thus avoiding costly calculations. In our previous work, we investigated their applicability to the properties of gold nanoparticles [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk

2023

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We analyse the efficacy of machine learning (ML) interatomic potentials (IP) in modelling gold (Au) nanoparticles. We have explored the transferability of these ML models to larger systems and established simulation times and size thresholds necessary for accurate interatomic potentials. To achieve this, we compared the energies and geometries of large Au nanoclusters using VASP and LAMMPS and gained better understanding of the number of VASP simulation timesteps required to generate ML-IPs that can reproduce the structural properties. We also investigated the minimum atomic size of the training set necessary to construct ML-IPs that accurately replicate the structural properties of large Au nanoclusters, using the LAMMPS-specific heat of the Au147 icosahedral as reference. Our findings suggest that minor adjustments to a potential developed for one system can render it suitable for other systems. These results provide further insight into the development of accurate interatomic potentials for modelling Au nanoparticles through machine learning techniques.

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“…Regarding the question of time and size thresholds, in Ref. [ 5 ] we used many data points to determine the ML-IAP of Au

to ensure we were benchmarking at a high accuracy. This made us wonder whether similar accuracy could be achieved with less data.…”

Section: Results and Discussionmentioning

confidence: 99%

“…In our previous investigation [ 5 ], we performed extensive MD calculations on the lowest energy structure of the Au

Section: Computational Detailsmentioning

confidence: 99%

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Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk

2023

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“…In this regard, the recently developed DeePMD-kit package, which relies on TensorFlow, a robust and common deep learning framework, can be used to parametrize the potentials for accurate and lengthy molecular dynamics runs. 53,54 Notably, the DFT calculations presented here can train next-generation classical force fields.…”

Section: ■ Settings and Modelsmentioning

confidence: 99%

Transforming Cl-Containing Waste Plastics into Carbon Resource for Steelmaking: Theoretical Insight

Assadi,

Doustkhah

2023

ACS Eng. Au

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The accumulation of waste plastics poses a significant environmental challenge, leading to persistent pollution in terrestrial and aquatic ecosystems. A practical approach to address this issue involves the transformation of postconsumer waste plastics into industrially valuable products. This study focuses on an example of harnessing the carbon content in these polymers for carbon-demanding industrial processes, thereby reducing waste plastics from the environment and alleviating the demand for mined carbon resources. Employing quantum simulations, we examine the viability of polychloroprene as a carburizing agent in the steelmaking process. Our simulations reveal that polychloroprene exhibits excellent carbon diffusivity in molten iron, with a theoretical diffusion coefficient of 8.983 × 10–5cm2 s–1. This value competes favorably with that of metallurgical coke and surpasses the carbon diffusivity of other polymers, such as polycarbonate, polyurethane, and polysulfide. Additionally, our findings demonstrate that the chlorine content in polychloroprene does not permeate into molten iron but instead remains confined to the molten iron and slag interface.

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“…The results show that the platinum single atoms and clusters supported on nitrogen-doped graphene nanosheets have greater activity for HER due to the high utilization of nearly all the platinum atoms [1]. Hence, a large number of investigations have been performed to explore the geometries, stabilities, electronic structures, and physical properties of clusters [7][8][9][10] using such methods as the first principles investigation [11][12][13], molecular dynamics [14,15], bionics algorithm [16][17][18], and machine learning [19][20][21], among which kinds of bionics algorithm are on the rise.…”

Section: Introductionmentioning

confidence: 99%

Structures, Electronic, and Magnetic Properties of CoKn (n = 2–12) Clusters: A Particle Swarm Optimization Prediction Jointed with First-Principles Investigation

et al. 2023

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Transition-metal-doped clusters have long been attracting great attention due to their unique geometries and interesting physical and/or chemical properties. In this paper, the geometries of the lowest- and lower-energy CoKn (n = 2–12) clusters have been screened out using particle swarm optimization and first principles relaxation. The results show that except for CoK2 the other CoKn (n = 3–12) clusters are all three-dimensional structures, and CoK7 is the transition structure from which the lowest energy structures are cobalt atom-centered cage-like structures. The stability, the electronic structures, and the magnetic properties of CoKn clusters (n = 2–12) clusters are further investigated using the first principles method. The results show that the medium-sized clusters whose geometries are cage-like structures are more stable than smaller-sized clusters. The electronic configuration of CoKn clusters could be described as 1S1P1D according to the spherical jellium model. The main components of petal-shaped D molecular orbitals are Co-d and K-s states or Co-d and Co-s states, and the main components of sphere-like S molecular orbitals or spindle-like P molecular orbitals are K-s states or Co-s states. Co atoms give the main contribution to the total magnetic moments, and K atoms can either enhance or attenuate the total magnetic moments. CoKn (n = 5–8) clusters have relatively large magnetic moments, which has a relation to the strong Co-K bond and the large amount of charge transfer. CoK4 could be a magnetic superatom with a large magnetic moment of 5 μB.

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Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles

Cited by 8 publications

References 35 publications

Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk

Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk

Transforming Cl-Containing Waste Plastics into Carbon Resource for Steelmaking: Theoretical Insight

Structures, Electronic, and Magnetic Properties of CoKn (n = 2–12) Clusters: A Particle Swarm Optimization Prediction Jointed with First-Principles Investigation

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