Towards computational polar-topotronics: Multiscale neural-network quantum molecular dynamics simulations of polar vortex states in SrTiO3/PbTiO3 nanowires

• Published in: Frontiers in nanotechnology Vol. 4
• Main Authors: Linker, Thomas, Fukushima, Shogo, Kalia, Rajiv K., Krishnamoorthy, Aravind, Nakano, Aiichiro, Nomura, Ken-ichi, Shimamura, Kohei, Shimojo, Fuyuki, Vashishta, Priya
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media SA 30.08.2022; Frontiers Media S.A
• Subjects: ferroelectrics; machine learning; neural-network quantum molecular dynamics; QM/MM simulation; topotronics
• ISSN: 2673-3013; 2673-3013
• DOI: 10.3389/fnano.2022.884149

Recent discoveries of polar topological structures ( e.g ., skyrmions and merons) in ferroelectric/paraelectric heterostructures have opened a new field of polar topotronics. However, how complex interplay of photoexcitation, electric field and mechanical strain controls these topological structures remains elusive. To address this challenge, we have developed a computational approach at the nexus of machine learning and first-principles simulations. Our multiscale neural-network quantum molecular dynamics molecular mechanics approach achieves orders-of-magnitude faster computation, while maintaining quantum-mechanical accuracy for atoms within the region of interest. This approach has enabled us to investigate the dynamics of vortex states formed in PbTiO 3 nanowires embedded in SrTiO 3 . We find topological switching of these vortex states to topologically trivial, uniformly polarized states using electric field and trivial domain-wall states using shear strain. These results, along with our earlier results on optical control of polar topology, suggest an exciting new avenue toward opto-electro-mechanical control of ultrafast, ultralow-power polar topotronic devices.