Neural Network Based Modeling of Grain Boundary Complexions Localized in Simple Symmetric Tilt Boundaries Σ3 (111) and Σ5 (210)

Korolev, Vadim V.; Mitrofanov, Artem; Nevolin, Yu. M.; Krotov, Vladimir; Ul’yanov, D. K.; Проценко, П.

doi:10.1134/s1061933x20050105

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…[4][5][6][7][8][9][10][11][12][13][14] The remarkable performance of MLIPs has already been demonstrated for inorganic solids, 2,15-20 hybrid materials, 21 water, 22,23 interfaces, [24][25][26][27][28] and the dynamics of defects in crystalline and amorphous materials. [29][30][31][32] A key component for developing reliable potentials in ML-based PES models is building a robust and representative reference dataset for model training. The training dataset is usually composed of atomic configurations with corresponding energies, forces, and/or stress tensors from DFT calculations.…”

Section: Introductionmentioning

confidence: 99%

An automated approach for developing neural network interatomic potentials with FLAME

Mirhosseini¹,

Tahmasbi²,

Kuchana³

et al. 2021

Preprint

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The performance of machine learning interatomic potentials relies on the quality of the training dataset. In this work, we present an approach for generating diverse and representative training data points which initiates with ab initio calculations for bulk structures. The data generation and potential construction further proceed side-byside in a cyclic process of training the neural network and crystal structure prediction based on the developed interatomic potentials. All steps of the data generation and potential development are performed with minimal human intervention. We show the reliability of our approach by assessing the performance of neural network potentials developed for two inorganic systems.

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