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

Kınacı, Alper; Narayanan, Badri; Şen, Fatih; Davis, Michael J.; Gray, Stephen K.; Sankaranarayanan, Subramanian K. R. S.; Chan, Maria K. Y.

doi:10.1038/srep34974

Cited by 29 publications

(41 citation statements)

References 64 publications

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“…In accordance with the DFT predictions, our AL-NN GM structures are planar for clusters with size n < 14 atoms and globular at higher sizes. Our predicted critical size for planar to globular transition (13 atoms) is identical to previous DFT calculations, [26,27] and previously reported ion mobility and spectroscopy measurements. [67] AL-NN predicted planar GM structures for sizes up to 13 atoms match the ones reported in Ref.…”

Section: Resultssupporting

confidence: 89%

“…[39] Note that DFT-PBE significantly underestimates this value (-2.97 eV) due to its inadequate treatment of the dispersion effects in Au. [46,26] AL-NN predictions for the elastic constants are in excellent agreement with experiments as well as spherically symmetric potentials (i.e., EAM, SC). In particular, one of the challenging elastic properties to describe is the ratio C 12 /C 44 .…”

Section: Resultssupporting

confidence: 53%

“…For instance, Au clusters in the sub-nanometer range undergo a planar-to-globular transition at cluster size of 13 atoms, which has proven to be very difficult for empirical potential models to capture. [26] Spherically symmetric potentials such as embedded atom method and Sutton Chen potentials cannot capture the planar configurations whereas bond-order potentials such as Tersoff perform well for planar structures but do not completely capture the size dependent structural transition in Au clusters. [27] It is well known that the use of predefined functional form imposes serious limitations on the physics and chemistry that can be captured.…”

Section: Introductionmentioning

confidence: 99%

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Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (∼ 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (∼ 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with ∼ 500 total reference calculations. Using an extensive DFT test set of ∼ 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/Å of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

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Section: Resultssupporting

confidence: 89%

Section: Resultssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

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Active learning a coarse-grained neural network model for bulk water from sparse training data

Loeffler

Patra

Chan

et al. 2020

Mol. Syst. Des. Eng.

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“…[145][146][147][148][149] More recently, detailed analysis of the planar-to-globular transition in small gold clusters has convincingly questioned that argument. 150 The planar structures for Pt n (n = 4 − 6) in Figure 20, which resulted from nonrelativistic computations, support the conclusions in Ref.…”

Section: Gas-phase Clusterssupporting

confidence: 74%

Ab-<i>initio</i> studies of adsorbate-surface interactions

Rêgo¹

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“…For instance, Au clusters in the sub-nanometer range undergo a planar-toglobular transition at cluster size of 13 atoms, which has proven to be very difficult for empirical potential models to capture. [28] Spherically symmetric potentials such as embedded atom method and Sutton Chen potentials cannot capture the planar configurations whereas bond-order potentials such as Tersoff perform well for planar structures but do not completely capture the size dependent structural transition in Au clusters. [41] It is well known that the use of predefined functional form imposes serious limitations on the physics and chemistry that can be captured.…”

Section: Introductionmentioning

confidence: 99%

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (� 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on-thefly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (�1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with �500 total reference calculations. Using an extensive DFT test set of~1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/ Å of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

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

Cited by 29 publications

References 64 publications

Active learning a coarse-grained neural network model for bulk water from sparse training data

Active learning a coarse-grained neural network model for bulk water from sparse training data

Ab-<i>initio</i> studies of adsorbate-surface interactions

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

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