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Meeting MS&T23: Materials Science & Technology
Symposium Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title Accurate Prediction of Oxygen Vacancy Concentration with Disordered A-site Cations in High-entropy Perovskite Oxides
Author(s) Jiyun Park, Boyuan Xu, Jie Pan, Dawei Zhang, Stephan Lany, Xingbo Liu, Jian Luo, Yue Qi
On-Site Speaker (Planned) Jiyun Park
Abstract Scope Entropic stabilized ABO3 perovskite oxides promise many applications, including the two-step solar thermochemical hydrogen (STCH) production. Using binary and quaternary A-site mixed {A}FeO3 as a model system, we reveal that as more cation types, especially above four, are mixed on the A-site, the cell lattice becomes more cubic-like but the local Fe–O octahedrons are more distorted. By comparing four different Density Functional Theory-informed statistical models with experiments, we show that the oxygen vacancy formation energies distribution and the vacancy interactions must be considered to predict the oxygen non-stoichiometry (δ) accurately. For STCH applications, the oxygen vacancy formation energy distribution, including both the average and the spread, can be optimized jointly to improve Δδ (difference of δ between the two-step conditions) in some hydrogen production levels. This model can be used to predict the range of water splitting that can be thermodynamically improved by mixing cations in {A}FeO3 perovskites.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accurate Prediction of Oxygen Vacancy Concentration with Disordered A-site Cations in High-entropy Perovskite Oxides
AI/ML Aided Drug Biomolecule and Materials Design
Autonomous Learning of Phase Trajectories via Physics-inspired Graph Neural Networks
B-1: Multi-objective Optimization for Improving Mechanical Properties of Aluminum Alloys: A Data Analytics Approach with Machine Learning and Genetic Algorithms
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Machine Learning-assisted Exploration of the Chemistry-processing Design Space Under Additive Manufacturing: Application to an FCC HEA Space
Machine Learning-based Prediction of the Mechanical Properties of Microalloyed Steel Subjected to Thermomechanical Controlled Processing
Machine Learning for Phase Prediction of High-entropy Alloys Assisted by Imbalance Learning
Online Mechanical Properties Control for Steel Coils Using Machine Learning Model
Optical and Photothermal Property Prediction of Gold Nanoparticle/polymer Hybrid Films Through Machine Learning and Finite Element Modeling
Optimizing Heat Treatment Routes for Ni-based Alloys Using Monte Carlo Tree Search
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Prediction of the Mechanical Response of Zirconia-reinforced Metal-matrix Composite Using Deep Learning Approaches
Process Cycle Modeling with AI
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