A Graph Dynamical neural network approach for decoding dynamical states in ferroelectrics.

Journal Article · Wed Apr 26 00:00:00 EDT 2023 · Carbon Trends

DOI:https://doi.org/10.1016/j.cartre.2023.100264· OSTI ID:1994704

Dhakane, Abhijeet ^[1]; Xie, Tian ^[2]; Yilmaz, Dundar E. ^[3]; Duin, Adri van ^[3]; Sumpter, Bobby G. ^[1]; Ganesh, P. ^[1]

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1. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
3. Pennsylvania State Univ., University Park, PA (United States)

Ferroelectric materials such as BaTiO₃ show tremendous potential for emerging advances in memory devices, particular neuromorphic type devices. High density of memory can be obtained by stabilising polar domain walls at the nanoscale, regions of discontinuity between the well-defined polarization order parameter, but little is known about what controls their structure and dynamics in real nanoscale materials. Indeed, chiral polar domain walls have been observed in heterogeneous ferroelectrics, such as oxygen-deficient BaTiO₃, but very little is known about how such polar-domains walls interact with defects. Indeed, a critical understanding of how dynamics of domain-walls depend on point-defects is crucial to create engineered ferroelectric memory devices. For this work, we perform large-scale simulations of nansocale domain-wall dynamics in pristine and defective BaTiO₃ using reactive force-field developed by us earlier (Phys. Chem. Chem. Phys., 2019, 21, 18240–18249), and capture their dynamical dependence on point defects using a graph dynamical neural-network approach, which we adapted to interrogate solids with well-defined order-parameters, and implemented using Pytorch based libraries. Our machine learning (ML) approach goes beyond the traditional post-processing methods to capture both spatial and temporal heterogeneities of large-scale molecular dynamics simulations of complex defective ferroelectric oxide materials. We crucially find that isolated oxygen vacancies introduce very localized spatial regions (~1–2 unit-cell in length) that show slow dipole relaxation due to formation of defect-dipoles, and that these defect-dipoles in turn slow the intrinsic dynamics of domain walls. Further, the roughness of domain walls, also influenced by vacancies, introduce dynamic heterogeneity along the domain-wall. As such we find a novel mechanism by which quenched disorder due to defects introduce dynamic heterogeneity thereby influencing response to external fields (particularly time varying fields) in a ferroelectric. Our study also emphasizes the need for creating digital twins of dynamical quantities to achieve autonomous in operando control of nanoscale switching.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1994704
• Journal Information: Carbon Trends, Journal Name: Carbon Trends Journal Issue: 1 Vol. 11; ISSN 2667-0569
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
digital twin
domain walls
ferroelectrics
machine-learning
neural-networks
phase-transitions