Combining ab initio accuracy and large-scale Molecular Dynamics with Machine Learning: an application on Transition Metal Dichalcogenides.

ORAL

Abstract

Amidst the exciting landscape of materials science, Transition Metal Dichalcogenides (TMDs) have emerged as exciting subjects of exploration. These versatile low-dimensional materials, which include monolayer TMDs like MoS2 and MoSe2, offer a rich tapestry of optical and dynamical properties with applications across various scientific domains. In our pursuit of understanding TMDs, we've employed a molecular dynamics method powered by neural network potentials. This innovative approach enables us to delve into these materials with remarkable precision, and we have determined phonon dispersion curves and frequency-dependent dielectric constants at finite temperatures for monolayer systems. Importantly, our approach is adaptable and can readily incorporate any new findings.

* We would like to thank the help from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de amparo a pesquisa do estado de minas gerais (FAPEMIG), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and the University of Georgia from making this research project possible. Also we would like to thank the Georgia Advanced Computing Resource Center (GACRC) for the for providing the computational resources that made this work possible.

–

Presenters

  • Gabriel Bruno Garcia de Souza

    University of Georgia

Authors

  • Gabriel Bruno Garcia de Souza

    University of Georgia

  • David P Landau

    University of Georgia

  • Von Braun Nascimento

    Universidade Federal de Minas Gerais

  • Steven B Hancock

    JHUAPL

  • Rosângela de Paiva

    Universidade Federal de São João del Rey

  • Yohannes Abate

    University of Georgia, Department of Physics and Astronomy, University of Georgia, Athens, GA