Neural network molecular dynamics of ferroelectric domain boundary
ORAL
Abstract
Oxide perovskites like PbTiO3 (PTO) are ferroelectric materials which exhibit spontaneous
polarization at ambient conditions. Formation of nanometer-size domains with opposite
polarizations separated by domain walls (DWs) essentially control the design and function of
ferroelectric devices. However, the underlying physics is not well understood and ab-initio
molecular dynamics (MD) simulation is computationally too costly to study DW structures.
Neural network MD (NNAIMD) is an emerging approach to study large-scale atomistic systems
with quantum mechanics accuracy. In this study, I will discuss the development of NNAIMD force
field to study PTO crystal, along with simulation results on complex DW dynamics using it.
polarization at ambient conditions. Formation of nanometer-size domains with opposite
polarizations separated by domain walls (DWs) essentially control the design and function of
ferroelectric devices. However, the underlying physics is not well understood and ab-initio
molecular dynamics (MD) simulation is computationally too costly to study DW structures.
Neural network MD (NNAIMD) is an emerging approach to study large-scale atomistic systems
with quantum mechanics accuracy. In this study, I will discuss the development of NNAIMD force
field to study PTO crystal, along with simulation results on complex DW dynamics using it.
*This work was supported as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award Number DE-SC0014607.
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Presenters
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Anikeya Aditya
- Univ of Southern California