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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Innovations in Energy Materials: Unveiling Future Possibilities of Computational Modelling and Atomically Controlled Experiments
Presentation Title From Prediction to Experimental Realization of Ferroelectric Wurtzite AlN-Based Alloys
Author(s) Cheng-Wei Lee, Keisuke Yazawa, Thi Nguyen, Nate Bernstein, Victoria Bradford, Geoff L. Brennecka, Prashun Gorai
On-Site Speaker (Planned) Cheng-Wei Lee
Abstract Scope AlN-based alloys find widespread application in high-power microelectronics and optoelectronics. The realization of ferroelectricity in wurtzite AlN-based heterostructural alloys has opened up the possibility of directly integrating ferroelectrics with conventional microelectronics based on tetrahedral semiconductors such as Si, SiC and III-Vs, enabling compute-in-memory architectures, high-density data storage, and more. The discovery of AlN-based wurtzite ferroelectrics has been driven to date by chemical intuition and empirical explorations. Here, we demonstrate the computationally-guided discovery and experimental demonstration of new ferroelectric wurtzite (Al,Gd)N and (Al,Hf)N alloys. We find that a change in the atomic-scale polarization switching pathway, from a high-barrier collective to an individual switching process with a lower overall energy barrier, is a strong indicator for experimentally observing polarization reversal near room temperature. We provide fundamental chemical insights by relating compositional and structural parameters such as bond length, ionicity, and strength to the trends in the switching barriers of AlN-based alloys.
Proceedings Inclusion? Planned:
Keywords Electronic Materials, Computational Materials Science & Engineering, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Development of kinetic lattice Monte Carlo model to study ionic diffusion at misfit dislocations in oxide heterostructures
Exploring Ultra-Stable Green Rust Compositions for Green Energy Catalysis
From Prediction to Experimental Realization of Ferroelectric Wurtzite AlN-Based Alloys
Local Thermal Conductivity Imaging and Modelling to Guide Microstructure Engineering in Energy Materials
Machine Learned Multiphysics Modeling: Enhancing Uniform Distribution of Low-Energy Lithium-Ion Transport Channels in Solid Electrolyte Interphase of Electrodes
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Nanoscale design of 3D anode and high effective catalysis for high performance Aluminum-air batteries
Optimization of CO2 Reduction Reaction Using Nanoporous Copper Catalysts through Machine Learning-Driven Process Parameter Modeling
Quantum-Assisted Machine Learning Analysis of Silicon-Based Anodes for Lithium Batteries: Thermodynamics, Structural Insights, and Lithium Diffusion. Identifying Challenges and Exploring Novel Candidates
Reaching new frontiers to for superconductors using pulsed high magnetic fields
Resonant Ultrasound Spectroscopy for Rapid Down Selection, Elastic Property Determination, and Model Validation in High-Entropy Materials
Specialized Machine Learning Interatomic Potential to assess Self-Healing at a W Grain Boundary
Starrydata2: an Open Platform for Materials Data Curated from Literature
Structure Low Dimensionality and Lone-Pair Stereochemical Activity: the Key to Low Thermal Conductivity in sulfides
The Exploration of FeNiMoW-based alloys for High Value Magnetic Materials
The Magic and Myths of Machine Learning in Materials Science
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Unraveling the Effects of Dislocations on Ferroelectric Behavior by Molecular Dynamics Simulations
Ab Initio Models for the Prediction of Corrosion-Passivation Behavior in Aqueous Media

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