ProgramMaster Logo
Conference Tools for MS&T24: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T24: Materials Science & Technology
Symposium Uncertainty Quantification Applications in Materials and Engineering
Presentation Title Unraveling Correlation between Interface Structure and Magnetic Properties of La1-xSrxCoO3−δ/La1-xSrxMnO3−δ Bilayers Using Neural Architecture Search and Deep Ensembles
Author(s) Amit Samanta, Hong Sun, Vincenzo Lordi, Yayoi Takamura
On-Site Speaker (Planned) Amit Samanta
Abstract Scope Interfaces between perovskite oxides, such as La1-xSrxCoO3−δ/La1-xSrxMnO3−δ (LSCO/LSMO) bilayers, exhibit unconventional magnetic exchange switching behaviors, charge and spin transfer behaviors, offering a pathway for innovative designs in perovskite oxides-based device. We leverage first-principles simulation, evolutionary algorithm, and neural network search with on-the-fly uncertainty quantification to design deep learning model ensembles to investigate over 50,000 LSCO/LSMO bilayer structures as a function of oxygen deficiency and strontium concentration. Our analysis reveals that non-uniform distributions of Sr ions and oxygen vacancies on both sides of the interface alters local magnetization at the interface, showing a transition from ferromagnetic to local antiferromagnetic regions. This provides valuable guidance for the design of perovskite oxide multilayers for target device application. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the LDRD Program (21-ERD-005).

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Parametric Study of Optical Floating-Zone Crystal-Growth Furnace Through Modeling of Heat Transfer: Effect of Sample Properties and Environment Gas Pressure
Automating Engineering Design with UQ-Aware Scientific Learning
Bayesian Calibration of Cladding Creep Model Coefficients in the PAD5 Fuel Performance Code Using the Dakota Toolkit
Bayesian Protocols for High-Throughput Optimization of Kinematic Hardening Models Using Cyclic Microindentation Experiments
Introduction to Verification, Validation, and Uncertainty Quantification for Engineering Simulation
Quantification of Uncertainty in Microstructure Segmentation of Solid Oxide Cell Electrodes Using an Improved Watershed Methodology
Quantitative Analysis of Systematic Uncertainties in Empirical and Machine Learning Interatomic Potentials
Tasmanian Toolkit for Uncertainty Quantification
Uncertainty Quantification in Machine Learning Models with High-Dimensional Features and Large Sample Size
Uncertainty Quantification of Material Properties in Data-Poor Regimes Using Transfer Learning and Gaussian Process Regression
Unraveling Correlation between Interface Structure and Magnetic Properties of La1-xSrxCoO3−δ/La1-xSrxMnO3−δ Bilayers Using Neural Architecture Search and Deep Ensembles
Questions about ProgramMaster? Contact programming@programmaster.org