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About this Abstract
Meeting	MS&T22: Materials Science & Technology
Symposium	AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title	Understanding Atomic-scale Mechanisms of Defect Dynamics in Rare Earth Nickelates by Machine Learning and Quantum Simulations
Author(s)	Mirza Galib, Badri Narayanan
On-Site Speaker (Planned)	Badri Narayanan
Abstract Scope	Perovskites rare earth nickelates are promising materials for neuromorphic computing architectures. The resistive switching in these materials can be induced via electron doping (e.g. creating oxygen vacancies[OVs]), however standard computational methods are prohibitively expensive to study the dynamics of the OVs over nano-meter and nano-second length and timescales. In order to bridge this gap, we have recently developed deep neural network (DNN) models from DFT+U data for SmNiO₃ as a representative of the rare earth nickelates. With these models, we have been able to investigate the atomistic details of the defect dynamics in SmNiO₃ over nanoscale length and time. Our models can also predict atomic bader charges and maximally localized wannier centers. In this talk, I will discuss the application of DNN models to unveil the correlation between the structural and electronic properties, and its impact on the transport barriers for OVs in oxygen-deficient rare-earth nickelates.

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