Structural studies of Au–Pd bimetallic nanoparticles by a genetic algorithm method

Shao, Guifang; Tu, Na-Na; Liu, Tundong; Xu, Liang-You; Ye, Wen

doi:10.1016/j.physe.2015.02.008

Cited by 9 publications

(11 citation statements)

References 38 publications

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“…, r n ). The latter approach is computationally much more efficient, and it allows one to extend NP structure simulations to larger NPs sizes (92,93,(101)(102)(103). However, the design of such empirical potential models is a complex task.…”

Section: Simulations Of Static Structure Modelsmentioning

confidence: 99%

Solving the Structure and Dynamics of Metal Nanoparticles by Combining X-Ray Absorption Fine Structure Spectroscopy and Atomistic Structure Simulations

Timoshenko

Duan

Henkelman

et al. 2019

Annual Rev. Anal. Chem.

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Extended X-ray absorption fine structure (EXAFS) spectroscopy is a premiere method for analysis of the structure and structural transformation of nanoparticles. Extraction of analytical information about the three-dimensional structure and dynamics of metal–metal bonds from EXAFS spectra requires special care due to their markedly non-bulk-like character. In recent decades, significant progress has been made in the first-principles modeling of structure and properties of nanoparticles. In this review, we summarize new approaches for EXAFS data analysis that incorporate particle structure modeling into the process of structural refinement.

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Section: Simulations Of Static Structure Modelsmentioning

confidence: 99%

Solving the Structure and Dynamics of Metal Nanoparticles by Combining X-Ray Absorption Fine Structure Spectroscopy and Atomistic Structure Simulations

Timoshenko

Duan

Henkelman

et al. 2019

Annual Rev. Anal. Chem.

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“…48 These models are considered critical for nanoparticle structure predictions to reduce computational time due to the large size of each predicted system. 46,47 However, the accuracy of the calculation is heavily dependent on which model is used as well as the accuracy of the model's approximations. 48 Swamy et al 48 used two separate force field models to predict all known polymorphs of TiO 2 with varying success depending on the specific polymorph.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Another method to calculate the energy of atomic configurations are empirical force field models. These simulate interatomic interactions in unique ways depending on the model. , These models are used when the system grows in complexity where ab initio calculations become too computationally demanding . These models are considered critical for nanoparticle structure predictions to reduce computational time due to the large size of each predicted system. , …”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

2018

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Predicting crystal structure has always been a challenging problem for physical sciences. Recently, computational methods have been built to predict crystal structure with success but have been limited in scope and computational time. In this paper, we review computational methods such as density functional theory and machine learning methods used to predict crystal structure. We also explored the breadth versus accuracy of building a model to predict across any crystal structure using machine learning. We extracted 24 913 unique chemical formulas existing between 290 and 310 K from the Pearson Crystal Database. Of these 24 913 formulas, there exists 10 711 unique crystal structures referred to as entry prototypes. Common entries might have hundreds of chemical compositions, while the vast majority of entry prototypes is represented by fewer than ten unique compositions. To include all data in our predictions, entry prototypes that lacked a minimum number of representatives were relabeled as “Other”. By selecting the minimum numbers to be 150, 100, 70, 40, 20, and 10, we explored how limiting class sizes affected performance. Using each minimum number to reorganize the data, we looked at the classification performance metrics: accuracy, precision, and recall. Accuracy ranged from 97 ± 2 to 85 ± 2%; average precision ranged from 86 ± 2 to 79 ± 2%, while average recall ranged from 73 ± 2 to 54 ± 2% for minimum-class representatives from 150 to 10, respectively.

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“…By using the coordinate ranking, the excellent individuals can pass down to the next generations during the genetic iterative evolution. We had already successfully used this improved GA to predict the stable structures of AuePd bimetallic NPs [25].…”

Section: Simulation Methodsmentioning

confidence: 99%

Structural optimization of Pt–Pd–Rh trimetallic nanoparticles using improved genetic algorithm

Liu

Shao

et al. 2016

Journal of Alloys and Compounds

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Structural studies of Au–Pd bimetallic nanoparticles by a genetic algorithm method

Cited by 9 publications

References 38 publications

Solving the Structure and Dynamics of Metal Nanoparticles by Combining X-Ray Absorption Fine Structure Spectroscopy and Atomistic Structure Simulations

Solving the Structure and Dynamics of Metal Nanoparticles by Combining X-Ray Absorption Fine Structure Spectroscopy and Atomistic Structure Simulations

Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

Structural optimization of Pt–Pd–Rh trimetallic nanoparticles using improved genetic algorithm

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