Studying nanostructures at the experimental scale often demands extremely large computational cells, encompassing thousands of atoms. This renders quantum-mechanical methods, such as density functional theory (DFT), impractical. Machine learning provides a solution to this challenge. By training interatomic potentials against DFT data, we can conduct simulations of potential energy surfaces and corresponding structures much more swiftly than with DFT alone, while maintaining the accuracy inherent in the quantum-mechanical approach. Our focus lies particularly on employing deep learning to train interatomic potentials for tasks, such as optimization, molecular dynamics, and phonon studies.
Dr Agnieszka Beata Kuc
Ekin Esme Bas
Dario Calvani
Beatriz Costa Guedes
Umm-e-Hani
Yingying Zhang
CASUS
Untermarkt 20, D-02826 Görlitz
Conrad-Schiedt-Str. 20, D-02826 Görlitz
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CASUS is an institute of Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
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