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Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

Engineering and Technology

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

S. Kang, W. Jeong, et al.

Experience a breakthrough in the discovery of inorganic compounds with SPINNER, a revolutionary structure-prediction framework developed by authors from Seoul National University. This innovative approach utilizes neural network potentials to accelerate crystal structure prediction up to 1000 times faster than traditional methods, unlocking the potential for large-scale exploration of new materials.

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Playback language: English
Abstract
Discovering multicomponent inorganic compounds is crucial for addressing scientific and engineering challenges, but the vast material space hinders experimental synthesis. Crystal structure prediction (CSP) offers a solution, but its computational cost remains a bottleneck. This paper introduces SPINNER, a structure-prediction framework using neural network potentials (NNPs) that accelerates CSP by 10<sup>2</sup>-10<sup>3</sup> times compared to DFT-based methods. SPINNER incorporates algorithms optimized for NNPs, achieving higher accuracy than conventional algorithms. Blind tests on 60 ternary compositions show SPINNER identifies experimental or theoretically more stable phases for ~80% of materials, outperforming data-mining and DFT-based evolutionary predictions. SPINNER paves the way for large-scale exploration of undiscovered inorganic crystals.
Publisher
npj Computational Materials
Published On
May 12, 2022
Authors
Sungwoo Kang, Wonseok Jeong, Changho Hong, Seungwoo Hwang, Youngchae Yoon, Seungwu Han
Tags
multicomponent compounds
crystal structure prediction
neural network potentials
accelerated computation
ternary compositions
material discovery
inorganic crystals

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