Automated discovery of a robust interatomic potential for aluminum

Smith, Justin S.; Nebgen, Benjamin; Mathew, Nithin; Chen, Jie; Lubbers, Nicholas; Burakovsky, Leonid; Tretiak, Sergei; Nam, Hai Ah; Germann, Timothy C.; Fensin, Saryu; Barros, Kipton

doi:10.1038/s41467-021-21376-0

Cited by 68 publications

(78 citation statements)

References 80 publications

(50 reference statements)

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“…ML-based interatomic potentials, therefore, are beginning to be applied to a range of challenging materials-science research questions, such as the modeling of phase-change memory materials, [12][13][14] catalysts, [15] or battery materials. [16] Recently, a number of "general-purpose" ML potentials have been reported, which can accurately describe a broad range of atomic configurations and materials properties-including silicon, [17] carbon, [18] aluminum, [19,20] and the binary Ga-As system. [21] The hope for such potentials is to enable "off-the-shelf" use without further modification: for example, the aforementioned silicon ML potential has been used to study complex structural transitions under pressure [22] or unusual mechanical properties of amorphous silicon (a-Si).…”

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confidence: 99%

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

2021

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such as "ab initio" molecular dynamics (AIMD). ML-based interatomic potentials, therefore, are beginning to be applied to a range of challenging materials-science research questions, such as the modeling of phase-change memory materials, [12][13][14] catalysts, [15] or battery materials. [16] Recently, a number of "general-purpose" ML potentials have been reported, which can accurately describe a broad range of atomic configurations and materials properties-including silicon, [17] carbon, [18] aluminum, [19,20] and the binary Ga-As system. [21] The hope for such potentials is to enable "off-the-shelf" use without further modification: for example, the aforementioned silicon ML potential has been used to study complex structural transitions under pressure [22] or unusual mechanical properties of amorphous silicon (a-Si). [23] The starting point for the present study is a general-purpose Gaussian approximation potential (GAP) ML model for bulk and nanostructured phosphorus-which was shown to be flexible enough to be applicable to the pressure-induced liquidliquid phase transition from the molecular fluid to the network liquid, whilst also accurately describing the crystalline allotropes and the layered structure of phosphorene. [24] This GAP is now set to facilitate even more challenging studies on more extended length or time scales, and the exploration of other structurally complex phases for which it has not been explicitly "trained", such as amorphous phosphorus (a-P).Research interest in a-P has grown because of emerging applications in batteries. [25][26][27][28] As a commercially available anode material, red phosphorus provides a large cation-storage ability with high theoretical capacities by forming binary X-P compounds (X = Li, Na, K), but it suffers from low conductivity and a large volumetric change during cycling. [29] As discussed in ref. [30], these issues can be ameliorated by creating composites of a-P and carbonaceous materials: on the one hand, increasing electronic conductivity; [31,32] on the other hand, minimizing the mechanical stress induced by volume changes. [30,33] The structure and properties of phosphorus itself are clearly important for battery applications: for example, experimental work showed that the size of a-P particles in phosphoruscarbon composite anodes has an effect on the electrochemical performance, [34] and in situ transmission electron microscopy (TEM) revealed images of red phosphorus segments within a carbon nanofiber. [35] Computationally, structurally ordered phases have been studied with density-functional theory (DFT; Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. Here, it is shown that large-scale molecular-dynamics simulations, enabled by a machine-learning (ML)-based interatomic potential for phosphorus, can give new insights into the atomic structure of a-P and how...

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Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

2021

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“…The generation processing of the interatomic potential of the bulk and the multilayer structure of hexagonal boron nitride use the GAP ML algorithm 64 . An ML data set construction method in which the data set is regularly retrained is used on elemental aluminum (ANI‐Al) 65 . The physically informed neural network (PINN) method is used to obtain the potential function for Al and accurately predicts some physical properties 66 .…”

Section: Applicationsmentioning

confidence: 99%

“…64 An ML data set construction method in which the data set is regularly retrained is used on elemental aluminum (ANI-Al). 65 The physically informed neural network (PINN) method is used to obtain the potential function for Al and accurately predicts some physical properties. 66 The grain boundary energies of fcc element metals (e.g., Al, Cu, Ag, Au, Pd, and Pt) are predicted by ML potential.…”

Section: Single Elementmentioning

confidence: 99%

Machine‐learning‐based interatomic potentials for advanced manufacturing

Wan

et al. 2021

Int Journal of Mech Sys Dyn

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This paper summarizes the progress of machine-learning-based interatomic potentials and their applications in advanced manufacturing. Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development of fast interatomic potential with ab initio accuracy. The accelerated atomic simulation can greatly transform the design principle of manufacturing technology. The most widely used supervised and unsupervised ML methods are summarized and compared. Then, the emerging interatomic models based on ML are discussed: Gaussian approximation potential, spectral neighbor analysis potential, deep potential molecular dynamics, SCHNET, hierarchically interacting particle neural network, and fast learning of atomistic rare events.

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“…49,70,104,105 For example, ANI-Al is a ML potential recently proposed for aluminum solid state simulations. 84 Although each AL MD simulation is initialized to a random disordered system (melts), after several iterations, the AL starts capturing ordered configurations like FCC, HCP, BCC, etc. (Figure 5A).…”

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The Rise of Neural Networks for Materials and Chemical Dynamics

Kulichenko

Smith

Nebgen

et al. 2021

J. Phys. Chem. Lett.

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Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.

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Automated discovery of a robust interatomic potential for aluminum

Cited by 68 publications

References 80 publications

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Machine‐learning‐based interatomic potentials for advanced manufacturing

The Rise of Neural Networks for Materials and Chemical Dynamics

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